GECCO '25 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)—another simple EDA—, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LeadingOnes benchmark—one of the most studied theory benchmarks in the domain of evolutionary computation.

We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LeadingOnes of problem size n. For the cGA's single parameter μ = Ω(n log² n), we prove that the cGA samples the optimum of LeadingOnes with high probability within O(μn log n) function evaluations. For the best choice of μ, our result matches, up to polylogarithmic factors, the typical O(n²) runtime that many randomized search heuristics exhibit on LeadingOnes.

This paper for the hot-off-the-press track at GECCO 2025 summarizes the work Marcel Chwiałkowski, Benjamin Doerr, and Martin S. Krejca: Runtime analysis of the compact genetic algorithm on the LeadingOnes benchmark. IEEE Transactions on Evolutionary Computation 2025. Early access. DOI: 10.1109/TEVC.2025.3549929 [3].

Deploying deep neural networks (DNNs) on microcontroller units (MCUs) is a common trend to process the increasing amount of sensor data generated at the edge, but it is challenging due to resource and latency constraints. Neural architecture search (NAS) helps automate the search for suitable DNNs. In our original work "Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML" [3], we present a novel NAS strategy for edge deployment using multi-objective Bayesian optimization (MOBOpt) and reinforcement learning (RL). Our approach efficiently balances accuracy, memory, and computational complexity, outperforming existing methods on multiple datasets and architectures such as ResNet-18 and MobileNetV3.

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) does not directly optimize a given multi-objective function f but instead optimizes N + 1 single-objective subproblems of f. We analyze for the first time how the MOEA/D with only standard mutation operators computes the whole Pareto front of the OneMinMax benchmark when the g-optima are a strict subset of the Pareto front. For standard bit mutation, we prove an expected runtime of O(nN log n + n^n/(2N)N log n) function evaluations. For the second phase, when the algorithm start with all g-optima, we prove an [EQUATION] expected runtime.

For power-law mutation with exponent β ∈ (1, 2), we prove an expected runtime of O(nN log n + n^β log n) function evaluations. The O(n^β log n) term from the second phase is independent of the number of subproblems N, leading to a huge speedup over standard bit mutation. In general, our bound for power-law suggests that the MOEA/D performs best for N = O(n^β-1), resulting in an O(n^β log n) bound.

This paper for the hot-off-the-press track at GECCO 2025 summarizes the work Benjamin Doerr, Martin S. Krejca, and Noé Weeks: Proven runtime guarantees for how the MOEA/D computes the Pareto front from the subproblem solutions. Parallel Problem Solving from Nature, PPSN 2024. Springer, 197–212. 10.1007/978-3-031-70071-2_13 [2].

We start the runtime analysis of multi-objective evolutionary algorithms for unbounded integer search spaces. We analyze single-and full-dimensional mutation operators with three different mutation strengths: changes by plus/minus one (unit strength), random changes following a law with exponential tails, and random changes following a power-law. We rigorously prove on a recently proposed benchmark that unit mutation can be slow when the initial solutions are far from the Pareto front. When setting the expected change right, exponential-tail mutation yields the best runtime guarantees in our results—however, with a wrong choice of this expectation, the performance guarantees quickly become uninteresting. With power-law mutation, we obtain good results uniformly over all problem parameters and starting points. We complement our mathematical findings with experimental results that suggest that our bounds are not always tight. Most prominently, power-law mutation outperforms the exponential-tail mutation even when the latter uses a near-optimal parametrization. Hence, we suggest to favor power-law mutation for unknown problems in integer spaces.

This paper for the hot-off-the-press track at GECCO 2025 summarizes the work Benjamin Doerr, Martin S. Krejca, and Günter Rudolph: Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces. Conference on Artificial Intelligence, AAAI 2025. AAAI Press. 26955–26963. [11].

The non-dominated sorting genetic algorithm II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcomings in discrete search spaces, namely, that the NSGA-II has difficulties with more than two objectives and that it is very sensitive to the choice of the population size. To overcome these difficulties, we analyze a simple tie-breaking rule in the selection of the next population. Similar rules have been proposed before, but have found only little acceptance. We prove the effectiveness of our tie-breaking rule via mathematical runtime analyses on the classic OneMinMax, LeadingOnesTrailingZeros, and OneJump-ZeroJump benchmarks. We prove that this modified NSGA-II can optimize the three benchmarks efficiently also for many objectives, in contrast to the exponential lower runtime bound previously shown for OneMinMax with three or more objectives. For the bi-objective problems, we show runtime guarantees that do not increase when moderately increasing the population size over the minimum admissible size.

This paper for the hot-off-the-press track at GECCO 2025 summarizes the work Benjamin Doerr, Tudor Ivan, and Martin S. Krejca. Speeding up the NSGA-II with a simple tie-breaking rule. Conference on Artificial Intelligence, AAAI 2025. AAAI Press. 26964–26972 [8].

This short paper summarizes the main results of our work A Performance Analysis of Lexicase-Based and Traditional Selection Methods in GP for Symbolic Regression that has been recently accepted for publication in the ACM journal Transactions on Evolutionary Learning and Optimization. In this paper, we analyze the performance of several relevant lexicase variants and two traditional selection methods in combination with down-sampling strategies on a wide range of symbolic regression benchmark problems. We perform experiments not only for a given evaluation budget, but also for different time budgets as the time complexity of the selection methods varies greatly. We find that the performance ranking of the selection methods depends on the given setting. For example, for a given evaluation budget, the best performing method is ϵ-lexicase selection combined with a down-sampling strategy, while for a short runtime, selection methods using batches of training cases perform best. Further, we find that tournament selection combined with informed down-sampling performs relatively well overall.

Bayesian optimisation (BO) is an efficient approach for solving expensive optimisation problems, where acquisition functions play a major role in achieving the tradeoff between exploitation and exploration. The exploitation-exploration tradeoff is challenging; excessive focus on exploitation can stagnate the search, while too much exploration can slow convergence. Multi-objectivisation has been explored as an effective approach to mitigate the exploitation-exploration tradeoff problem. Along this line, in this paper, we propose a Multi-Objectivisation-based adaptive Exploitation-Exploration tradeoff framework (MOEE) to balance exploitation and exploration in BO. MOEE considers the nondominated front formed by the exploitation and exploration objectives and adaptively switches the focus on exploration and exploitation on the basis of the search status. We verify our method on the 19 synthetic and practical problem instances with up to 20 dimensions, and the results show that our proposed multi-objectivisation framework can achieve a good balance between exploitation and exploration.

This paper for the Hot-off-the-Press track at GECCO'25 summarises the work [4]: Chao Jiang and Miqing Li. "Multi-objectivising acquisition functions in Bayesian optimisation." ACM Transactions on Evolutionary Learning and Optimization (2025).

This paper addresses theory in evolutionary multiobjective optimisation (EMO) and focuses on the role of crossover operators in many-objective optimisation. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives. We present a many-objective problem class together with a theoretical runtime analysis of the widely used NSGA-III to demonstrate that crossover can yield an exponential speedup on the runtime. In particular, this algorithm can find the Pareto set in expected polynomial time when using crossover, while without crossover it requires exponential time to even find a single Pareto-optimal point. To our knowledge, this is the first rigorous runtime analysis in many-objective optimisation demonstrating an exponential performance gap when using crossover for more than two objectives. This Hot-off-the-Press paper summarises the work Andre Opris, A Many-Objective Problem Where Crossover is Provably Indispensable, To appear at The 39th Annual AAAI Conference on Artificial Intelligence, 2025.

This paper, originally published in Evolutionary Computation Journal [11], investigates the interplay between surrogate model performance, model settings, and black-box landscape features within the context of Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our focus is on understanding how landscape characteristics influence surrogate model accuracy during evolutionary optimization, aiming to inform the automated selection and tuning of surrogate models. We perform a comprehensive feature analysis, identifying robust and informative landscape features relevant to surrogate modeling. The analysis explores the error dependencies of four models across 39 settings, utilizing three methods for input data selection, drawn from surrogate-assisted CMA-ES runs on noiseless benchmarks within the Comparing Continuous Optimizers framework. The insights gained can help in the development of adaptive surrogate modeling strategies and metalearning approaches for evolutionary computation.

Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups (clusters) based on some distance measure, such that the data points in the same group are similar while that in different groups are dissimilar. Due to its importance and NP-hardness, a lot of methods have been proposed, among which Evolutionary Algorithms (EAs) are a class of popular ones. Evolutionary clustering has found many successful applications, but all the results are empirical, lacking theoretical support. This paper fills this gap by proving that the approximation performance of the GSEMO (a simple multi-objective EA (MOEA)) for solving four formulations of clustering, i.e., k-tMM, k-center, discrete k-median and k-means, can be theoretically guaranteed. Furthermore, we consider clustering under fairness, which tries to avoid algorithmic bias, and has recently been an important research topic in machine learning. We prove that for discrete k-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.

This paper for the Hot-off-the-Press track at GECCO 2025 summarizes the work Chao Qian. Can Evolutionary Clustering Have Theoretical Guarantees? IEEE Transactions on Evolutionary Computation, 2024, 28(5): 1220–1234. [24]

Quality-Diversity (QD) algorithms are a new type of Evolutionary Algorithms (EAs), aiming to find a set of high-performing, yet diverse solutions. They have many successful applications in reinforcement learning and robotics, helping improve the robustness in complex environments. Furthermore, they often empirically find a better overall solution than traditional search algorithms which explicitly search for a single highest-performing solution. However, their theoretical analysis is far behind. In this paper, we try to shed some light on the optimization ability of QD algorithms theoretically. By comparing the popular QD algorithm MAP-Elites with (μ + 1)-EA (a typical EA focusing on finding better objective values only), we prove that on two NP-hard problems, i.e., monotone approximately submodular maximization and set cover, MAP-Elites can achieve the (asymptotically) optimal polynomial-time approximation ratio, while (μ + 1)-EA requires exponential expected time on some instances. This provides theoretical justification for that QD algorithms can be helpful for optimization, and discloses that the simultaneous search for high-performing solutions with diverse behaviors can provide stepping stones to good overall solutions and help avoid local optima.

This paper for the Hot-off-the-Press track at GECCO'25 summarizes the work C. Qian, K. Xue, and R.-J. Wang. Quality-Diversity Algorithms Can Provably Be Helpful for Optimization (IJCAI'24). [20]

This work explores the use of doubly stochastic matrices within Estimation of Distribution Algorithms to solve permutation-based problems, focusing on the Quadratic Assignment Problem (QAP). Experiments show DSMs outperform other models in both efficiency and effectiveness, with potential applicability to ordering problems like and other optimization methods.

EVOTER is a framework for evolving transparent, interpretable rule sets that combine predictive accuracy with human readability. Unlike post-hoc explainers, EVOTER constructs intrinsically interpretable models through list-based grammatical evolution. The framework introduces three innovations: time-lag operators, feature-feature comparisons, and nonlinear transformations. It demonstrates strong performance across diverse domains including classification, time-series forecasting, and policy learning. By enabling models that are both effective and auditable, EVOTER advances the state of explainable artificial intelligence.

Learning Fuzzy-Classifier Systems (LFCSs), also known as evolutionary fuzzy rule-based machine learning, combine evolutionary algorithms with fuzzy rules to create interpretable models. However, traditional inference schemes lack mechanisms to quantify uncertainty in predictions. This paper introduces a novel class inference scheme based on the Dempster-Shafer Theory of Evidence that explicitly models epistemic uncertainty through an "I don't know" state. By calculating belief masses for each class hypothesis and uncertainty, our approach enhances robustness in real-world applications. Experiments on real-world datasets demonstrate statistically significant improvements in classification performance compared to conventional approaches. Our method forms smoother decision boundaries and provides quantitative measures of prediction confidence, addressing a critical gap in reliable decision-making for fuzzy rule-based machine learning.

This paper summarizes the ACM TELO article: Hiroki Shiraishi, Hisao Ishibuchi, and Masaya Nakata. 2025. A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems. ACM Transactions on Evolutionary Learning and Optimization. https://doi.org/10.1145/3717613 [7]. Our implementation is available at https://github.com/YNU-NakataLab/jUCS.

Interpretable machine learning (ML) implies that structural relationships to exist between different parts of the ML model. We demonstrate how the tangled program graph (TPG) framework is able to demonstrate structural relationships for a suite of partially observable reinforcement learning tasks. The tasks are defined in terms of developing spell casting behaviours for the Invoker hero under the Dota 2 game engine. We show that TPG is able to demonstrate all 4 of the properties used to define interpretable machine learning. Moreover, a unique form of feature engineering / modularity takes place between programs that define state for (indexed) memory versus programs defining actions. The full article appears as Smith and Heywood (2024) "Interpreting Tangled Program Graphs under Partially Observable Dota 2 Invoker tasks" in IEEE Transactions on Artificial Intelligence, 5 (4): 1511–1524. https://doi.org/10.1109/TAI.2023.3279057

Demand for goods delivery services keeps growing, creating problems with last mile delivery. While autonomous delivery robots provide a solution to many of these problems, configuring their deployment to balance cost, performance, and safety is complex. Our industry partner Panasonic uses a multi-objective search approach to find configurations for their system, leveraging a simulator to assess the quality of the different configurations. However, due to the complexity of the simulator, this approach proved very computationally expensive. In this work, we propose an approach that uses a surrogate model to speed up fitness computation. Since building the surrogate model has a cost itself, the approach trains the model incrementally during the search: first, an imprecise model is used, while, as the search progresses, more precise models are used. The search stops when no improved solutions can be found. Experimental results show that this new approach, while it produces configurations of slightly lower quality, requires significantly less computing time. This is an extended abstract of "Alternating Between Surrogate Model Construction and Search for Configurations of an Autonomous Delivery System" by C.-H. Sun, T. Laurent, P. Arcaini, and F. Ishikawa, IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024).

We showcase work on "Evolutionary algorithms for solving single- and multiple-objective political redistricting problems: The case study of Poland" published in the Applied Soft Computing Journal 152 (2024) 111258. It proposes and evaluates new advanced evolutionary methods for solving political redistricting problems. The reported experiments involve diverse settings, among which the most extensive concerned a simultaneous optimization of four criteria: equality of populations in election districts, districts compactness, the dissimilarity of the districting plan with the one being in effect, and the number of mandates attainable by a selected party. Our algorithms demonstrate low computational complexity and the ability to well-approximate the Pareto front. The latter also proved meaningful for conducting a post-analysis, allowing us to understand how realizations of specific optimization criteria correlate.

This paper summarizes our recent work published in IEEE Transactions on Evolutionary Computation (2025), entitled "LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics". In this work we propose a novel evolutionary algorithm framework that leverages large language models (LLMs) like GPT-4 to automatically generate, evaluate, and refine metaheuristic optimization algorithms. Evaluations on the BBOB benchmark suite demonstrate that algorithms automatically discovered by LLaMEA can outperform established state-of-the-art methods, such as CMA-ES and DE, especially on lower-dimensional optimization problems. Our results illustrate the significant potential of using LLMs for automated algorithm design, setting a new direction for evolutionary computation research.

This paper introduces IGJSP, a Benchmark Generator for Job Shop Scheduling Problems (JSP), which generates customized instances to evaluate optimization algorithms. JSP requires assigning tasks to jobs and machines while minimizing makespan, energy consumption, and tardiness, key aspects for industrial process optimization and energy-efficient scheduling. IGJSP fills a gap by providing flexible, reproducible benchmarks that mirror real-world complexities, unlike existing libraries such as JSPLIB that do not support modern multi-objective scenarios essential for advanced optimization techniques.

IGJSP supports large-scale instance generation incorporating energy efficiency, job flexibility, and dynamic constraints. The tool offers multiple configuration options and outputs, including JSON, DZN, and Taillard formats, ensuring compatibility with both complete solvers and modern heuristic or metaheuristic algorithms. Its modular design allows precise customization of parameters such as the number of jobs, machine configurations (with speed scaling), processing times, and statistical distributions. By enabling rigorous algorithm comparisons and fostering reproducibility and meta-learning, IGJSP provides a trusted, scalable framework for testing diverse heuristics and metaheuristics. This contribution promotes standardized benchmarking practices and supports the development of adaptive, efficient algorithms, making it an essential resource for research and algorithm evaluation in scheduling problems. Overall, IGJSP significantly advances cutting-edge scheduling research and practice, providing robust solutions.

We introduce autoverse, a symbolically parameterizable Neural Cellular Automata (NCA)-based game engine for single-player grid-worlds. Users specify a set of spatial rewrite rules, which are sufficient for implementing a broad swath of the types of game environments (e.g. mazes, dungeons, sokoban puzzles) that currently serve as popular testbeds for Reinforcement Learning (RL) agents. These are compiled to a convolutions (i.e. an NCA), allowing for environments to be parallelized on the GPU, thereby drastically accelerating RL training. Meanwhile, rules and levels are encoded as binary strings, and applying mutations to these strings results in new, admissible environments, making autoverse a natural fit for evolutionary PCG.

We use autoverse to explore a novel method for automatically generating sets of complex game worlds, wherein environments are evolved so as to maximize the number of iterations required by greedy tree search to discover a new best solution. We find that spending more time in this evolutionary loop results in training sets that produce more general agents in terms of their performance on novel, held-out tasks. We use the compressibility of environment rollouts as a proxy measure of complexity and characterize evolved environments in an initial step toward generating more interpretable and aligned autoverse worlds.¹

Recent advances in quantum computing have shown great potential for solving combinatorial optimization problems. A notable example is the quadratic knapsack problem, a constrained binary optimization problem. However, current quantum-inspired algorithms require Quadratic Unconstrained Binary Optimization (QUBO) formulations. Transforming constrained optimization into QUBO impacts the algorithms' speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess them by using Simulated Annealing (SA), and find that three of our approaches outperform existing methods in terms of execution time as well as quality and quantity of feasible solutions found. Also, we tested these transformations on a quantum annealer, which was unable to solve even small instances, due to limitations in connectivity and error rates. In general, our results highlight the advantages of the new approaches, which reduce the number of variables in the QUBO representation.

This paper introduces L-DEGR, a hybrid metaheuristic designed to solve the NP-hard Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). L-DEGR combines the L-SHADE method with the Greedy Schedule Builder (GSB) proposed in the Differential Evolution (DE) Greedy (DEGR) algorithm, incorporating several enhancements to improve its effectiveness. Despite DE generally being used for continuous optimization, we showcase that it can be effectively employed to solve combinatorial MS-RCPSP. L-DEGR was compared to the state-of-the-art method on the iMOPSE d36 instance set, as well as to L-SHADE using two different greedy approaches. Both L-DEGR and L-SHADE methods were calibrated using the Taguchi design-of-experiment method, thus ensuring robust parameter configurations and a fair comparison. Additionally, this study introduces new, significantly larger industrial-size problem instances for MS-RCPSP, which expands the scope of evaluation and demonstrates the scalability and robustness of the proposed method. Furthermore, a CP-SAT approach was used to compute reference solutions for all instances, providing a reliable lower bound for comparison. Experimental results demonstrate that L-DEGR achieves statistically significant results in comparison to state-of-the-art methods, setting new best-known makespans for the MS-RCPSP iMOPSE d36 and large-scale instances.

As machine learning algorithms are increasingly employed for critical decision-making, ensuring algorithmic fairness has become imperative. Fairness-aware Automated Machine Learning has emerged as a flexible and effective method for enhancing both model accuracy and fairness. However, most existing studies focus on hyperparameter optimization for a single model. In this study, we frame the problem as a multi-objective Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem, aiming to jointly optimize both accuracy and fairness across a diverse set of machine learning algorithms and their corresponding hyperparameters. To address this challenge, we apply Multi-Objective Grammatical Evolution (MOGE). The results demonstrate that MOGE not only effectively identifies models that achieve higher fairness and accuracy, while also exploring the trade-offs between accuracy and fairness efficiently.

Local Interpretable Model-agnostic Explanations (LIME) is a widely used technique in machine learning for generating instance-level explanations by fitting simple surrogate models to approximate the predictions of black-box models. However, a key limitation of LIME is that it operates on a very small local region around an instance, which may yield trivial explanations. This weakness can be evaluated qualitatively via a novel fidelity metric we introduce, K-Neighbours Fidelity (K-NF), which measures how well an explanation is faithful to the black-box model on a supralocal (i.e., larger than local) region. Additionally, the commonly used linear surrogates in LIME often fail to capture non-linear relationships present in the underlying model. To address these issues, we propose SLIME (SupraLocal Interpretable Model-agnostic Explanations), a novel method that uses evolutionary symbolic regression (ESR) to build concise equation-based surrogates that function as explanations faithful to the supralocal region around an instance. By replacing LIME's linear surrogate with equation-based models generated via ESR, we demonstrate that SLIME has greatly improved fidelity over LIME across a wide range of experiments on real-world datasets from UCI and PMLB.

Reinforcement Learning (RL) offers a fundamental framework for discovering optimal action strategies through interactions within unknown environments. Recent advancement have shown that the performance and applicability of RL can significantly be enhanced by exploiting a population of agents in various ways. Zeroth-Order Optimization (ZOO) leverages an agent population to estimate the gradient of the objective function, enabling robust policy refinement without estimating auxiliary functions such as value functions or advantage functions. As another application, genetic algorithms (GA) boosts the exploration of policy landscapes by mutational generation of policy diversity in an agent population and its refinement by selection. A natural question is whether we can have the best of two worlds that the agent population can have. In this work, we propose Ancestral Reinforcement Learning (ARL), which synergistically combines the robust gradient estimation of ZOO with the exploratory power of GA. The key idea in ARL is that each agent within a population infers gradient by exploiting the history of its ancestors, i.e., the ancestor population in the past, while maintaining the diversity of policies in the current population as in GA.

Surrogate-assisted constrained multi-objective evolutionary algorithms are popular for solving expensive constrained multi-objective optimization problems. They build computationally cheap surrogate models to replace time-consuming function evaluations. In this way, algorithms can obtain optimal solutions within a limited number of function evaluations. However, most surrogate models are built for approximating each expensive objective function and constraint. These models may accumulate approximation errors into a nonnegligible level, and their frequent updates bring tremendous computational overhead. In this paper, a large language model (LLM)-assisted constrained multi-objective evolutionary algorithm (LECMO) is proposed. LECMO adopts the two-stage evolutionary search strategy. In the first stage, an existing surrogate-assisted evolutionary algorithm ignoring constraints is applied to drive solutions across infeasible regions. In the second stage, an LLM-assisted constrained multi-objective evolutionary algorithm is proposed to locate the constrained Pareto front. Experimental results on well-known test suites demonstrate the superiority of LECMO over compared algorithms.

The convergence and diversity of a population significantly influence the performance of evolutionary multi-objective optimization (EMO) algorithms. This paper introduces a novel quantitative metric for multi-objective optimization problems, termed spreadability, which serves as a guide for algorithm selection and performance evaluation of EMO algorithms. Spreadability is defined as the proportion of the decision space where a given solution remains mutually nondominated, calculated as the mathematical expectation of this proportion across all solutions. To support empirical analysis, we design several test problems with adjustable spreadability and conduct comprehensive experiments to evaluate the diversity-preserving capabilities of three classical EMO algorithms. The results indicate that MOEA/D-M2M with a diversity enhancement mechanism demonstrates superior performance. Furthermore, we propose survival and mortality rates for solutions based on spread-ability to intuitively demonstrate the impact of the enhancement mechanism on population diversity.

Multi-objective optimization algorithms aim to identify a set of non-dominated solutions that capture the complex and often conflicting trade-offs between objectives. In black-box settings, where the input-output relationship is usually analytically intractable, these algorithms face two major challenges: limited evaluation budgets, especially when objective evaluations are costly, and computational difficulties in proposing new candidates. While all non-dominated solutions hold equal mathematical value, the Decision-Maker is typically interested in only the most preferred trade-offs. This adds an additional challenge for preference-driven optimization: the cost of human interaction. To address these challenges, this paper proposes leveraging surrogate-assisted evolutionary multi-objective optimization methods, combined with advanced discretization techniques and strategic filtering of preferred solutions to mitigate the overall computational burden. After evaluating against similar methods in the literature, the experimental results suggest the method to be competitive in enhancing both the efficiency and precision of the decision-making process.

The Travelling Thief Problem (TTP) is a multi-objective, combinatorial, NP-hard optimisation problem with constraints and interwoven objectives resembling real-world problems. This paper proposes a Cluster-Based Region Selection Genetic Algorithm (CRSGA) utilising clusterisation of individuals coupled with a 2-layer selection operator: (1) tournament density-based selection that favours regions with sparsely distributed individuals, for which (2) individuals from those clusters are selected into pairs for reproduction. CRSGA focuses on balancing the exploration and exploitation of clusters, improving the quality of the results. The performance of the CRSGA is investigated on 24 benchmark instances with varying complexity. CRSGA outperforms 5 state-of-the-art methods.

Counterfactual explanations effectively interpret model decisions by identifying input modifications that lead to different outputs. However, generating realistic and actionable counterfactuals is challenging due to the lack of methodologies that effectively capture complex feature relationships and user-imposed constraints. This study introduces GraCo, a novel counterfactual generation (CG) method driven by Grammatical Evolution. GraCo automatically incorporates feature-domain knowledge and user preferences to generate plausible and actionable counterfactuals. We evaluate its effectiveness through empirical validation against state-of-the-art methods across multiple datasets. We propose a goodness metric for CG that accounts for class probability shifts and the differences between the counterfactual and the original input. GraCo achieves an average goodness score of 0.6231 across four datasets, outperforming all the compared approaches.

Dynamic constrained multiobjective optimization problems (DC-MOPs) are multiobjective optimization problems in which both the objectives and constraints can change over time. Prediction-based dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) have demonstrated superior performance by predicting initial population that is hopefully near the Pareto-optimal set (PS) in each new environment. However, the simultaneous changes in both the objectives and constraints make it difficult for these DCMOEAs to predict an initial population actually near the PS. Through observation, it has been found that the PS of DCMOPs typically lies along the path converging to the unconstrained PS (UPS). With this in mind, this paper introduces a novel DCMOEA based on Convergence Path Interpolation (CPI-DCMOEA). Specifically, when the environment changes, CPI-DCMOEA employs an autoencoder to predict the estimated UPS in the new environment. Then, CPI-DCMOEA uses Optimal Transport (OT) to approximate the convergence path toward the estimated UPS, which is defined as the trajectory from the random population to this estimated UPS. Finally, CPI-DCMOEA generates new solutions in batches by interpolating along this convergence path, with the interpolation parameter adaptively adjusted, ultimately producing a high-quality population in promising regions. Experimental results on DCF test suite show that the proposed CPI-DCMOEA has a significant advantage.

Drones are increasingly adopted in logistics due to their flexibility and efficiency, presenting novel solutions for complex service systems. This study develops a bi-objective optimization model for medical sampling service systems, where technicians and drones independently collect samples from patient locations. The model addresses critical challenges by simultaneously minimizing system completion time and patient test kit waiting duration, while incorporating realistic constraints, such as technicians' dynamic velocity variations reflecting traffic conditions and drone energy consumption dependent on load. A hybrid algorithm combining Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Tabu Search is proposed, ensuring elite solution preservation and genetic diversity. Experimental validation demonstrates better performance compared to NSGA-II, Tabu Search, and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) across hypervolume and non-dominated solution metrics, highlighting the approach's effectiveness in optimizing medical sampling logistics with disease outbreaks serving as a compelling use case.

Multi-objective optimization (MOO) aims to optimize multiple conflicting objectives simultaneously. Gradient-free methods are widely used in MOO problems. However, in some scenarios, such as multitask learning, gradient-free algorithms are not suitable due to the high dimensionality of the solution space. Thus, many gradient-based multi-objective optimization methods have been proposed to find the Pareto front effeciently. However, these methods suffer from the difficulty of escaping local optima. In this paper, we propose a multiple gradient descent algorithm based on decomposition (MGDA/D), which can easily escape from the local optima and find a set of good and diverse Pareto solutions. MGDA/D first decomposes a multi-objective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously by gradient descent. When the solution of a subproblem falls into local optima, MGDA/D uses information from its neighboring subproblems to update the solution to escape. Experimental results confirm that MGDA/D outperforms the state-of-the-art algorithms on both benchmark problems and multi-task learning applications.

To improve Covariance matrix adaptation evolution strategy (CMA-ES), which needs tuning of hyperparameters for optimizing multimodal and noisy problems, learning rate adaptation CMA-ES (LRA-CMA-ES) was proposed. This method adapts the learning rates based on the signal-to-noise ratio (SNR) of the update directions. On several high-dimensional optimization problems, part of the design variable affects the evaluation value and other variables are redundant. This property is called low effective dimensionality (LED). Although LRA-CMA-ES shows robust performance on multimodal and noisy problems, its performance is deteriorated by LED. To improve the performance on functions with LED, this paper introduces the countermeasures for LED to LRA-CMA-ES. The proposed method, LRA-CMA-ES-LED, estimates the element-wise SNR on the direction along the eigenvector of the covariance matrix to compute the effectiveness of each direction. We reduce the influence of redundant directions on the learning rate adaptation mechanism based on the estimated effectiveness. In the numerical experiments, we used benchmark functions in tens of dimensions containing 10 effective directions, which shows the performance improvement on multimodal and noisy problems with LED.

In several real-world applications, each element of the solution incurs a cost, such as manufacturing or computational costs. Considering the limitation of the total cost, the desired solution contains some parts tuned in the high-cost space and other parts selected from the low-cost set. We term these optimization problems as the search space selection problem, which has a constraint on the cost computed by which subspace the corresponding part of the solution belongs. This paper considers the case where each dimension consists of the low-cost integers and the high-cost complementary set in the continuous space. Then, we propose an optimization method for the search space selection problem by extending a mixed-integer optimization method, (1+1)-CMA-ES with margin, which incorporates the margin correction to prevent premature convergence in the discrete space. We first address the stagnation that occurs when directly applying existing constraint handling in mixed-integer optimization. The proposed method also considers the additional variables for search space selection and applies the margin correction to both the dimensions for search space selection and the selected dimensions as low-cost integer space. Numerical simulations show that the proposed method could find better solutions than comparative methods on the search space selection problem.

Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques.

Deep Optimization (DO) is a recently proposed metaheuristic that uses deep neural networks for optimization. It shows promise solving various Combinatorial Optimization (CO) problems; however, it only operates on binary data, requiring problems to be transformed using Quadratic Unconstrained Binary Optimization (QUBO). The study explores the use of DO for the Single-Stage Job Scheduling Problems (SSJSP), a constrained CO problem. As SSJSP does not naturally fit QUBO, these problems require a workaround to use the number partitioning problem as part of the transformation process and introduce binary and Lagrangian penalty methods to integrate the constraints as part of DO. Performance testing across various parameter settings using generated datasets with controlled slack demonstrates that the method works well and DO with penalty integration performs satisfactorily on all datasets. Both penalty integration methods are found to be effective in guiding DO toward feasible and optimized solutions, however their relative effectiveness varies depending on the specific scenario.

Evolutionary algorithms to solve multi-modal and multi-objective optimization problems do not follow any specific order of convergence to different optimal solutions. However, there exist certain problems, in which a hierarchical discovery of multiple critical solutions is must to constitute the desired set of solutions. A recently introduced innovation path (IP) problem is one such problem. In solving such problems, point-based algorithms, which find one targeted solution in a single application, becomes relevant. In this paper, we address this important aspect of convergence behavior of an evolutionary multi-objective optimization (EMO) algorithm visa-vis a point-based algorithm. The challenges of both approaches are discussed with examples. Results on a number of problems show that despite the apparent advantage of a point-based approach for finding hierarchical solutions one by one, the proposed population-based EMO approach enables a flexible and global search for finding multiple IP solutions in a single run.

Code review is an essential practice for software quality assurance. However, code review can be cumbersome as patches often undergo multiple rounds to fix bugs, enforce coding standards, and improve structure before merging or abandonment. Predicting the number of review rounds can help developers prioritize tasks and streamline the process. Existing machine learning models for review round prediction suffer from key limitations. Their black-box nature makes them difficult to interpret, reducing trust and adoption. Additionally, they rely on data re-balancing techniques that introduce artificial points, causing concept shifts and reducing reliability. To address these issues, we propose MORRP, a novel Multi-Objective Review Rounds Prediction approach. MORRP is based on Multi-Objective Genetic Programming (MOGP) to predict review rounds. Our method evolves interpretable models while optimizing precision, recall, and specificity without relying on data re-balancing. We evaluate our approach on three large open-source projects: Eclipse, OpenDaylight, and OpenStack. Results show that MORRP achieves competitive performance, with a micro F1 score between 0.65 and 0.75, outperforming complex ML models like Random Forest and LightGBM.

In high-dimensional real-world datasets, a critical challenge for feature engineering (FE) in machine learning (ML) is that it must balance predictive accuracy with feature interpretability. This paper introduces a novel nested genetic programming (GP) approach that includes evolutionary feature construction integrated with mutual information (MI)-based feature selection. Our approach adopts a custom GP fitness function based on minimal-redundancy-maximal-relevance (mRMR) criterion, guiding the evolutionary process to construct features that maximize mutual information with target variables while minimizing redundancy among the constructed features. The proposed approach addresses the critical challenge mentioned above by improving predictive performance while yielding interpretable features that are backed by information theory principles. Extensive experiments across diverse datasets demonstrate that our approach, even when utilizing a substantially reduced feature count, consistently outperforms both baseline methods using all original features and other contemporary FE techniques when compared at the same feature dimensionality. The effectiveness of our approach is validated across multiple ML models in regression tasks, showing improved accuracy and better generalization across diverse datasets and varying feature dimensionality.

Genetic programming (GP) is an evolutionary approach to the automated synthesis of computer programs. A critical challenge in GP is balancing population diversity and efficiency to prevent premature convergence while avoiding excessive program growth, commonly referred to as bloat. This work introduces a new syntactic-based crossover mechanism, Heterosis-based Crossover (HBC), which prioritizes structural differences over semantic criteria. Specifically, HBC enforces mating constraints by employing a structural diversity metric that restricts mating between individuals deemed too similar, effectively preventing "inbreeding." Through a series of symbolic regression tasks, we investigate how HBC influences population diversity, introns (structurally non-effective code), and bloat. Our findings reveal that HBC reduces premature convergence, preserves diversity, and mitigates bloat, enabling more effective exploration of the solution space. This study demonstrates that syntactic-based techniques offer a robust alternative to semantic-aware methods, emphasizing the importance of structural diversity in optimizing the trade-offs among exploration, exploitation, and computational resources in GP.

Genetic Programming (GP) faces two major challenges: the inability to use knowledge from past problems, as each run is independent, and the difficulty in identifying building blocks in the early stages of evolution. These limitations result in high computational costs and slower convergence. Transfer learning can provide a solution by leveraging past experiences to enhance performance in GP. A mutual-information-based transfer learning method is proposed in this paper to identify and transfer beneficial knowledge fragments. The method is evaluated on ten polynomial and trigonometric symbolic regression problems from previous literature and compared with standard GP and SubTree50 as state-of-the-art methods. Results of the above experiment demonstrate improved or comparable performance of the proposed method in terms of accuracy, statistical significance, and generalization. The contribution of this paper includes: a mutual-information-based technique for identifying and transferring knowledge fragments. Results highlight the potential of mutual information to address key challenges in GP effectively.

Fitness landscapes in test-based program synthesis are known to be extremely rugged, with even minimal modifications of programs often leading to fundamental changes in their behavior and, consequently, fitness values. Relying on fitness as the only guidance in iterative search algorithms like genetic programming is thus unnecessarily limiting, especially when combined with purely syntactic search operators that are agnostic about their impact on program behavior. In this study, we propose a semantics-aware search operator that steers the search towards candidate programs that are valuable not only actually (high fitness) but also only potentially, i.e. are likely to be turned into high-quality solutions even if their current fitness is low. The key component of the method is a graph neural network that learns to model the interactions between program instructions and processed data, and produces a saliency map over graph nodes that represents possible search decisions. When applied to a suite of symbolic regression benchmarks, the proposed method outperforms conventional tree-based genetic programming and the ablated variant of the method.

This work considers a realistic variant of the Electric Vehicle Charging Scheduling Problem (EVCSP), formulated to maximize the number of satisfied charging requests under grid capacity and charger-specific constraints. A bi-level optimization framework is introduced, where the upper level handles request-to-charger assignment using several Hybrid Genetic Algorithms (HGAs) enhanced by reinforcement learning techniques—Q-learning, Double Q-learning, SARSA, and Expected SARSA—to dynamically guide mutation decisions. The lower level applies an integer programming model to ensure feasible energy allocation under operational constraints. Extensive experiments on diverse benchmark instances demonstrate that RL-guided HGAs achieve substantial performance gains over existing methods, with the Q-learning-based variant consistently delivering the best results, particularly in large-scale and complex scenarios. These results highlight the effectiveness of integrating evolutionary search, adaptive learning, and mathematical programming for resource-constrained scheduling problems.

In this poster, we introduce a rank-based surrogate-assisted variant of CMA-ES. Unlike previous methods that employ rank information as constraints to train an SVM classifier, our approach employs a linear-quadratic regression on the ranks. We investigate the method's invariance empirically. While this first algorithm outperforms CMA-ES with a few exceptions, it falls short to entirely meet the lq-CMA-ES performance levels. To address this, we propose an enhanced variant that handles together two alternative surrogates, one based on the ranks and one based on the original function values. Although this variant sacrifices strict invariance, it gains in robustness and achieves performance comparable to, or even exceeding, lq-CMA-ES on transformed problems. This last algorithm shows how simply incorporating new transformations of rank values could improve any surrogate-based CMA-ES variant.

Molecular Property Prediction (MPP) plays a critical role in accelerating drug discovery by efficiently predicting the molecular characteristics. Despite advancements in multi-modal MPP methods, different modalities capture molecular features from different perspectives and different MPP tasks require specific modal features. Therefore, selecting the optimal combination of molecular modalities remains an open challenge. To achieve high accuracy while minimizing the number of modal features and model parameters, this paper proposes a modality-adaptive MPP method based on an improved multi-objective optimization algorithm. The molecular modalities we leverage include the pre-trained ChemBERTa model based on SMILES, six different molecular fingerprints, molecular graphs and 3D conformations. The improved multi-objective optimization algorithm integrates Topological Data Analysis (TDA), the teacher learning mechanism and graph centrality measures, achieving more diverse exploration and faster convergence. TDA refines the initial population for the uniform distribution, teacher learning mechanism directs offspring toward the Pareto front, and graph centrality measures combined with crowding distance enhance exploration of sparse regions. The experiments are conducted on three classification tasks and two regression tasks. Compared with the state-of-the-art methods, our method achieved the promising performance, demonstrating the effectiveness and adaptability of our method in improving molecular property prediction.

Recently, Convolutional Neural Networks (CNNs) have performed well in pattern recognition tasks such as image segmentation. However, designing a CNN for a particular dataset requires experience in choosing from feature extraction and aggregation operators. Neural Architecture Search (NAS) is automating the design of neural networks for a specific task. By leveraging its syntax tree representation, genetic programming (GP) can be adapted as a search strategy in the NAS (ENAS) problem. Solutions evaluation implies the models' weight optimization, making ENAS computationally expensive. Nonetheless, surrogate models can reduce the costly evaluations by predicting fitness solutions. Generating a surrogate model from variable-size tree-based GP solutions usually requires a tree-to-structured data conversion to use standard baseline regression models. This paper compares several surrogate models used to assist GP-based algorithms. Also, phenotype-level features are proposed to be included in converting syntax-tree solutions to sequence-vectors. The surrogate models are applied to optimize U-Net-type networks, showing promising results in reducing the number of costly evaluations and the execution time without affecting the segmentation performance.

This study addresses a variant of the Electric Vehicle Charging Scheduling Problem (EVCSP), which aims to maximize the number of satisfied charging requests while adhering to grid capacity and charger availability constraints. A hybrid approach is proposed that decomposes the problem into two stages: EV-to-charger assignment and energy allocation. For the assignment stage, two methods are evaluated: a heuristic method and a variable neighborhood search (VNS) algorithm. Both methods are integrated with an exact mathematical model at the second stage to optimize energy delivery for each assignment configuration. Experimental results show that the VNS algorithm consistently provides the best solution quality, outperforming both the heuristic method and existing solutions from the literature.

This paper addresses the dynamic economic emission dispatch (DEED) problem by integrating EVs as a flexible demand-side resource for peak shaving and valley filling considering the widespread adoption of Gridable Vehicles (GVs). The DEED problem introduces additional complexities of balancing energy supply and demand over time compared to traditional Emission Dispatch (EED). In order to enhance adaptability and exploration, the proposed Local Search Memory Algorithm based on Decomposition (LSMOMAD) combines dynamic crossover and mutation strategies to improve global search efficiency and prevent premature convergence. Additionally, an adaptive local improvement strategy accelerates convergence and avoids local optima. The simulation results demonstrate the effectiveness of the proposed algorithms when applied to the IEEE-30 and IEEE-39 bus systems and compared with the results from current state-of-the-art approaches.

A novel evolutionary computation technique known as genetic network programming-based rule mining, which accumulates outcomes obtained throughout the evolutionary process, has been developed to discover sets of itemset (ItemSB) with statistically distinctive backgrounds. This method builds upon traditional association rules by expanding the consequent part to include statistical expressions, such as correlation coefficients. In this study, we propose a method to extend the statistical representation part of ItemSB from correlation between two variables to an approximate logistic function. The rule set obtained using the proposed method can be used to add probability prediction to class classification by introducing a functional treatment to rule-based class classification. By varying the values of the variables treated in the logistic function, the transition of the predicted probability can be obtained; the flexible rule representation of ItemSB enables the identification of the items that are responsible for the change in predicted probability, thereby improving the explanatory nature of the prediction. The validation results of the proposed method on a medical dataset show that the proposed method enables predictions that consider explanatory and individual characteristics.

Cybersecurity refers to the practice of protecting hardware and software from cyberattacks, unauthorised access, theft, or damage and is becoming an increasing priority for organisations. A key question is the selection of measures (controls) to invest in to reduce the risk of a cybersecurity breach while keeping investments at a minimum. The contributions of this work are to (i) formulate this task as a constrained bi-objective problem, (ii) provide several realistic use cases varying in complexity for algorithm validation, and (iii) investigate the suitability of evolutionary multi-objective optimisation (in our case, MOEA/D) and an augmented epsilon-constraint approach (in CPLEX) to tackle the problem. We find that the augmented epsilon-constraint approach can solve the problem efficiently, capturing a diverse set of Pareto optimal solutions for each scenario. Although the performance of MOEA/D improves as the complexity of the problem increases, it is not able to compete with the augmented epsilon-constraint approach in terms of solutions found and reliability. We hope that the proposed problem and use cases will serve as an interesting test bed to benchmark optimisation algorithms and expand the problem formulation further.

The pdfo library by Tom M. Ragonneau and Zaikun Zhang makes the five derivative-free solvers BOBYQA, COBYLA, LINCOA, NEW-UOA, and UOBYQA—originally written by Michael J. D. Powell—available in Python. In this paper, we are comparing their performance on the bbob test suite with three other solvers from the COCO data archive: CMA-ES from pycma, SLSQP and BFGS from scipy. We also compare the original solvers, written by Powell in Fortran 77, with the current pdfo versions, which saw multiple bug fixes and code improvements by Ragonneau and Zhang.

For the latter comparison, we do not see large effects on performance between the Fortran 77 version and the current pdfo version. The only notable exception is the Bent Cigar function where we observe differences by a factor of 2–5 for BOBYQA, LINCOA, and NEWUOA. Compared to the other baseline algorithms, BOBYQA, LINCOA and NEWUOA perform very similarly over all bbob functions, being about a factor of 5 slower than SLSQP and BFGS while UOBYQA—as the best-performing pdfo solver—outperforms SLSQP and BFGS for larger budgets when compared over all 24 bbob functions. The linear surrogate of COBYLA, on the contrary, is clearly worse over all functions than the other algorithms.

UOBYQA, short for Unconstrained Optimization By Quadratic Approximation, is one of the well-known solvers derived and implemented by Michael J. D. Powell. In each step, the algorithm builds a quadratic surrogate of the objective function, interpolating quadratically many points for which the true function values are known. The model is optimized within the so-called trust region and the resulting solution is evaluated next. Adaptation of the trust region radius allows for fast convergence on a wide range of (noiseless) functions without the need for derivatives.

In this workshop paper, we investigate the effect of (frozen) nonnegative, i.e., worsening noise on UOBYQA with varying probability of solutions being affected by the noise. To this end, we use the COCO platform and its newest addition, the noiser, applied to the classical bbob functions. The numerical benchmarking experiments showcase that UOBYQA is negatively affected by the noise, but surprisingly little over a wide range of noise strengths for some of the bbob functions.

In this paper, we propose a comparative benchmark of seven multiobjective optimization methods from the PlatEMO platform using the COCO framework for performance assessment and the bi-objective test suite bbob-biobj. A mix of both standard methods and newer techniques from various sources that were implemented in the PlatEMO framework was selected for the comparison. We find that although the selected methods were overall mostly inferior to the top-performing methods from the bbob-biobj data archive, they performed quite well in certain computational budget ranges and specific function subgroups.

Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with respect to additional objectives or constraints that are difficult to capture into the modeling process. Aiming for batches of diverse solutions of high quality is often desirable, as it provides flexibility to accommodate post-hoc user preferences. At the same time, it is crucial that the quality of the best solution found is not compromised.

One particular problem setting balancing high quality and diversity is fixing the required minimum distance between solutions while simultaneously obtaining the best possible fitness. Recent work by Santoni et al. [arXiv 2024] revealed that this setting is not well addressed by state-of-the-art algorithms, performing in par or worse than pure random sampling.

Driven by this important limitation, we propose a new approach, where parallel runs of the covariance matrix adaptation evolution strategy (CMA-ES) inherit tabu regions in a cascading fashion. We empirically demonstrate that our CMA-ES-Diversity Search (CMA-ES-DS) algorithm generates trajectories that allow to extract high-quality solution batches that respect a given minimum distance requirement, clearly outperforming those obtained from of-the-shelf random sampling, multi-modal optimization algorithms, and standard CMA-ES.

This paper presents BEACON, a novel methodology for generating bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Although numerous benchmark problems exist, continuous benchmarks lack systematic mechanisms to control objective correlations, critical in real-world optimisation. Our approach utilises Gaussian Process samples approximated via Random Fourier Features and a Cholesky-based correlation transformation to generate problems with tunable correlation values ranging from perfectly negative to perfectly positive. Experiments with three popular multi-objective evolutionary algorithms (NSGA-II, SMS-EMOA, MOEA/D) across varying correlation levels and decision space dimensions reveal that algorithm performance depends on the interplay between correlation structure and dimensionality rather than either factor in isolation. Our framework bridges the gap between discrete benchmarks with correlation control and continuous benchmarks without it, enabling systematic study of correlation effects on optimisation dynamics and supporting the development of algorithms that can adapt to different correlation structures found in real-world problems.

Over the years, MOEA/D has proven to be a highly effective and efficient algorithm for solving complex problems. Consequently, several studies have focused on extending MOEA/D to address constrained multi-objective optimization problems. In this article, we introduce a dynamic penalty function into the MOEA/D framework to handle these constraints. This penalty function is based on the method of annealing penalties, modified to exhibit linear behavior when interacting with feasible and infeasible solutions during the search process. The proposed approach enables MOEA/D to manage constraints effectively. To assess the performance of our method, we evaluated it on the well-known complex problems from the CEC'2009 benchmark set, while comparing it gainst several state-of-the-art algorithms. Our empirical results show that the proposed method delivers competitive solutions and, in some cases, outperforms the MOEAs included in the comparative study.

Transport administrators make decisions about road infrastructure and traffic management that significantly impact people's lives. To improve efficiency, decision makers complement their expertise with the automated insight afforded by robust and reliable computational models of traffic. To produce such models, we propose an innovative intelligent algorithm that combines Symbolic Regression with Transfer Learning and Neural Networks. The algorithm learns historical and real time traffic patterns from several areas of the road network and transfers that knowledge to the location where a prediction is needed (i.e., the target zone), by injecting it into the model trained on local data. We enhanced our evolutionary algorithm with a Deep Learning component that automates the selection of areas to transfer knowledge from (i.e., the source zones). Its role is to identify those regions of the road network where pretraining models significantly increases predictive accuracy at target locations, providing decision makers with reliable foresight into traffic dynamics which is conducive to sounder road management strategy. Preliminary experiments show our approach is more likely to identify adequate transfer learning sources than algorithm variants where source selection is manual and, respectively, performed by a standard Artificial Neural Network.

Many real-world optimization problems are black-box optimization (BBO) problems. In the last decades, various algorithms have been designed to solve the complex BBO problems. These algorithms demonstrate great performance complementarity in practice, which means that different algorithms outperform on different problem instances. Consequently, how to realize Algorithm Selection (AS) emerges to be an important problem, which has attracted considerable attention. Unfortunately, despite the various methods proposed by researchers, including classification, regression, and ranking, there is still no consensus on which specific method is superior. To pave the researches of algorithm selection in the community, in this work, we employ Exploratory Landscape Analysis (ELA) for feature extraction and neural network as the selection model to study the effectiveness of different algorithm selection approaches. We implement various formulation methods and conduct extensive experiments to analyze their strength and weakness.

Our results reveal that classification-based approaches have significant advantages on selection accuracy, while regression-based approaches have better overall performance on the metrics. Meanwhile, the methods that utilize order information can generally serve as a compromise between classification-based methods and regression-based methods. For the convenience of reproduction and following works, we open-source our code at https://zenodo.org/records/14877548.

In Symbolic Regression (SR), achieving a proper balance between accuracy and interpretability remains a key challenge. The Genetic Programming variant of the Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is of particular interest as it achieves state-of-the-art performance using a template that limits the size of expressions. A recently introduced expansion, modular GP-GOMEA, is capable of decomposing expressions using multiple subexpressions, further increasing chances of interpretability. However, modular GP-GOMEA may create larger expressions, increasing the need to balance size and accuracy. A multi-objective variant of GP-GOMEA exists, which can be used, for instance, to optimize for size and accuracy simultaneously, discovering their trade-off. However, even with enhancements that we propose in this paper to improve the performance of multi-objective modular GP-GOMEA, when optimizing for size and accuracy, the single-objective version in which a multi-objective archive is used only for logging, still consistently finds a better average hypervolume. We consequently analyze when a single-objective approach should be preferred. Additionally, we explore an objective that stimulates re-use in multi-objective modular GP-GOMEA.

Evolutionary computation (EC) methods are effective optimisation algorithms, but their random nature and sensitivity to noise make their behaviour difficult to interpret, limiting real-world use. This paper introduces the novel visualisation tools Noisy Local Optima Networks (Noisy LONs) and Noisy Search Trajectory Networks (Noisy STNs) to illustrate algorithm behaviours and fitness landscapes affected by noise. We evaluated (1+1)-EA, UMDA, and PCEA on the OneMax and Knapsack combinatorial optimisation problems with additive Gaussian noise in fitness evaluations. Results show these visualisations can identify how noise affects algorithm performance, solution paths, and transitions between optima, aiding the explanation of why algorithms may succeed or fail in noisy environments. Further work should improve the clarity and scalability of visualisations to support application to a wider range of problems and noise types, as well as further investigate the predictive power of visualisation features.

In this paper, we introduce pyHMS, a comprehensive open-source Python implementation of the Hierarchic Memetic Strategy (HMS) algorithm, designed to address complex multimodal optimization problems. HMS employs a tree-like structure where multiple populations managed by different metaheuristics operate with limited communication, enhancing solution diversity while balancing exploration and exploitation through hierarchical organization. Our implementation consolidates various HMS advancements from the literature and in some cases provides expanded versions of what was presented. The code architecture is modular and easily extendable, supporting both a high-level interface for practitioners and detailed control for researchers, facilitating experimentation with novel configurations and hybridization of different metaheuristics. The library also provides a set of ready-made tools, which include implementations of niching techniques, widely recognized and novel optimization algorithms, visualization capabilities, and landscape approximation methods. The goal of pyHMS library is to provide a toolbox for tackling complex real-world problems and to encourage the use of this framework as a basis for the creation of novel, hybrid optimization algorithms.

This paper presents a Genetic Programming based framework called logicGP for classification with literal based models and the open source software created and used for the implementation. The used prediction model is a linear combination of logical monomials. The underlying algorithm is designed to be interpretable and to allow for a high degree of predictive ability. The implementation of the algorithm is done in C# with open source code and is designed for high compatibility with Microsoft's ML.NET. The algorithm is presented as an extensible framework and different parameterizations for different tasks are discussed. Additionally, the paper presents the open source code created for the framework and discusses the software architecture used. The algorithm is tested on simulated data for binary classification with three categories (inspired by the analysis of SNP data) and on selected real data for multiclass classification. The experiments show that the algorithm is able to achieve a high degree of predictive ability while maintaining a high degree of interpretability.

We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular implementations of Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, organized around three core components: (1) algorithm construction with reusable modules, (2) efficient benchmark problem suites, and (3) parallel experimental analysis. Experimental evaluations demonstrate the framework's superior performance in benchmark testing and algorithm comparison. Case studies further validate its capabilities in algorithm hybridization and parameter analysis. Compared to existing frameworks, SEvoBench demonstrates three key advantages: (i) highly efficient and reusable modular implementations of PSO and DE algorithms, (ii) accelerated benchmarking through parallel execution, and (iii) enhanced computational efficiency via SIMD (Single Instruction Multiple Data) vectorization for large-scale problems.

Cellular automata have long been celebrated for their ability to generate complex behaviours from simple, local rules, with well-known discrete models like Conway's Game of Life proven capable of universal computation. Recent advancements have extended cellular automata into continuous domains, raising the question of whether these systems retain the capacity for universal computation. In parallel, neural cellular automata have emerged as a powerful paradigm where rules are learned via gradient descent rather than manually designed. This work explores the potential of neural cellular automata to develop a continuous Universal Cellular Automaton through training by gradient descent. We introduce a cellular automaton model, objective functions and training strategies to guide neural cellular automata toward universal computation in a continuous setting. Our experiments demonstrate the successful training of fundamental computational primitives — such as matrix multiplication and transposition — culminating in the emulation of a neural network solving the MNIST digit classification task directly within the cellular automata state. These results represent a foundational step toward realizing analog general-purpose computers, with implications for understanding universal computation in continuous dynamics and advancing the automated discovery of complex cellular automata behaviours via machine learning. ¹

In developing brains, axonal projections follow chemical gradients shaped by local interactions. This paper asks whether such a process can be inferred from its outcome: For instance, given observed mouse brain connectivity, can one recover the developmental program that produced it? If such developmental programs can be recovered, they not only explain how biological connectivity arises, but also offer a biologically grounded search space for artificial intelligence, in which architectures emerge through the evolution of genetic encodings that produce plausible wiring diagrams. A framework is proposed that uses the biological connectome itself as a beacon to guide this search, referred to as the Connectome-Generating, AI-Generating Algorithm (CONGA). Implemented as neural cellular automata (NCA), a model was trained to reproduce axon-tracing data in the mouse connectome, and its internal representations were compared to gene expression patterns measured in the same spatial coordinates. The result demonstrates how the brain of an intelligent organism may self-assemble through an indirect encoding of connectivity. The model outperformed a static linear baseline, but only when constrained in size, suggesting that compact developmental programs better align with biological mechanisms.

We propose a novel, type-consistent representation for programs manipulating arbitrary data types, that we call typed token processing networks (TTPNs). A TTPN is a network of interconnected stateless gates defining typed ports and processing functions: during the execution, data flows through the network as typed tokens carrying values. TTPNs favor interpretability as they can visually reveal the overall structure of a program and also highlight the way data is processed at runtime—enabling decomposability and simulatability, respectively. Moreover, like other graph representations, TTPNs enable component reuse. We evolve programs in the form of TTPNs from examples, i.e., we do program synthesis, with a simple genetic algorithm and ad hoc genetic operators. Our preliminary results show successful evolution of simple programs from small example sets involving diverse types, though some instances fail. We hypothesize that the particularly rugged fitness landscape imposed by our representation and, more in general, by the program synthesis scenario, may hinder convergence. We propose some directions for tackling these issues.

This work presents a comparative analysis of thermal models for photovoltaic modules using Grammatical Evolution (GE) and Differential Evolution (DE) across four photovoltaic (PV) technologies: crystalline silicon (c-Si), amorphous silicon (a-Si), cadmium telluride (CdTe), and organic (OPV), under three sky conditions: sunny, cloudy, and diffuse. Temperature data were collected through a monitoring system in a photovoltaic cube, measuring temperatures on the horizontal face (top PV module) as well as environmental parameters (irradiance, ambient temperature, wind speed and direction, relative humidity). Three empirical models from the literature (Sandia, Faiman, and Obiwulu) were compared with 10 models generated by GE+DE using a Global Performance Index (GPI) to evaluate the accuracy of the models, considering five statistical metrics. The results show that in 11 out of 12 scenarios, the generated models outperform the empirical models, highlighting the importance of relative humidity in model accuracy. This work extends previous research, providing more accurate predictive models for the operating temperature of photovoltaic modules.

In this study, the Coverage Path Planning (CPP) problem is addressed, which aims to find the best flight path for a unmanned aerial vehicle (drone) to completely cover a given area of interest. Arbitrary areas containing obstacles (e.g. buildings, towers, trees or hills) are considered. The CPP is modeled as a Traveling Salesman Problem, where the drone must visit all non-obstacle points of the area to be covered. Battery power is the main resource that limits a drone's flight time or distance. It is known that, battery consumption is higher when a drone makes turns and altitude changes. To minimize the drone's energy consumption, we consider minimizing the total distance traveled, altitude variations and direction changes made during its route. As the CPP problem is NP-hard, in this work we propose a heuristic based on the GRASP meta-heuristic. The GRASP heuristic repeatedly constructs a solution using the cheapest insertion algorithm and this solution is improved by a local search procedure. The solutions determined by the proposed GRASP are compared with solutions generated by the Mixed-Integer Linear Programming model (solved by GUROBI solver). The results show that the GRASP heuristic determines excellent quality solutions using significantly less CPU time than GUROBI.

This work presents a nested multi-objective optimisation approach for constructing a digital twin of an aircraft's wingtail section from a high-dimensional finite element model (FEM). While FEMs are widely used in engineering for dynamic analysis, they may not always accurately represent physical structures due to simplifying assumptions employed in their development. In this study, an FEM is calibrated to measured wingtail data through model updating, a process where its parameters are adjusted to better match real-world observations. Conventional approaches to model updating require multiple evaluations of the FEM, which might not be viable for Large FEMs that are computationally expensive to evaluate. In this study, this is mitigated using a projection-based reduced-order model (ROM), which is also calibrated. The choice of ROM affects both the accuracy of the model in representing measured data and its computational efficiency—two critical objectives for digital twins. To optimise these conflicting objectives, a nested multi-objective optimisation constructs a set of effective ROMs. Results show that these ROMs significantly outperform those built using conventional methods. These findings demonstrate the potential for real-time applications of digital twins with finite element models, offering a flexible and efficient tool for model updates that adapt to varying operational conditions.

Efficient traffic signal timing is essential for smooth urban traffic flow, but traditional methods struggle with unpredictable conditions. This paper introduces a Memory-Assisted Genetic Algorithm (MGA) to optimize signal timing in such complex environments. The MGA incorporates two key features to enhance its performance. First, it leverages historical traffic data for more informed initial estimates, speeding up the optimization process. Second, it uses immigrants to introduce new solutions in each iteration, maintaining a balance between exploitation with learning from existing solutions patterns and exploring new possibilities to better map with the dynamicity of the traffic network. Tests on a realistic traffic model in SUMO show that the MGA outperforms standard Genetic Algorithm and Webster, delivering efficient and more effective solutions. It significantly reduces average waiting times and lowers fuel consumption. The approach is demonstrated on a real-world city network taken from the UK. These results highlight the MGA's potential as a powerful tool for dynamic and adaptable traffic signal control in urban settings.

Transportation in urban areas is becoming a challenge due to pollution, congestion, and difficulty in expanding current roads. By using existing waterways, autonomous electric vessels have become a sustainable maritime alternative to road transportation. For autonomous electric ferries to become a viable mode of public transportation, they must not only be more convenient, but also provide comfort and be completely safe to use. As these vessels navigate intricate water channels, sharing routes with traditional maritime traffic and responding to dynamic environmental conditions, a robust form safety validation is needed. However, performing such a safety validation is a complex task.

Maritime adaptive stress testing (MAST) is a novel testing method designed to perform safety validation of autonomous maritime systems by revealing the most likely sequences of events resulting in failure. Although promising, existing MAST methods have certain limitations, including the limited variety found among scenarios leading to failures, including collisions. In order to address these limitations, we integrate MAST with an evolutionary algorithm (EA), creating a novel EA-MAST method. Using simulations with two different collision avoidance systems for an autonomous electric ferry, we demonstrate the benefit of EA-MAST, including its capability to create novel and surprising stress test scenarios.

In this paper, we used real data to predict the position of the floating wind turbines due to three external forces: wind, wave and current. We analysed data provided by energy company Equinor and applied two machine-learning techniques: Multilayer Perceptron and Random Forest. After demonstrating that machine learning models failed, we used a simple linear regression model and optimisation approach to solve the multi-objective optimisation problem. We minimised the area of the wind farm and maximised its power output by applying the multi-objective optimisation algorithm NSGA-II. We also investigated how changing the length of mooring lines affected the optimisation. We used hypervolume to measure the algorithm's performance. We have shown that the results are very similar for fixed and floating wind farms. However, we have found that in complex wind conditions, i.e., when the wind does not blow only from one direction, for many wind turbines, the floating wind farms were a better solution than the fixed ones.

Transformer neural networks have gained popularity in recent years, demonstrating remarkable performance across many application domains. However, inference on resource-constrained embedded hardware remains challenging due to Transformers' substantial computational demands. We aim to address this problem by focusing on exploiting the inherent parallelism opportunities presented by the multi-head self attention operations of Transformers, to achieve a speedup in processing on embedded hardware. In this paper, we present an evolutionary-based scheduling approach for distribution and allocation of Transformer operations across systolic array-based hardware accelerators used for execution. Our methodology takes as input specifications of the Transformer workload and the target systolic array architecture and explores the large mapping space to identify an efficient plan of operation-to-array assignments. The plans are evaluated against a hardware-aware cost model, capturing the cost of computational cycles for a given operation and systolic array, with the objective to minimize the total sum across all operations. Through extensive experimental evaluations across diverse systolic array dimensions, we demonstrate that our evolutionary-based scheduler surpasses conventional heuristics and is able to find plans offering up to 33.8% average reduction in overall cycle count.

With the ever increasing capabilities of modern AI systems comes a greatly growing interest among various non-technical stakeholders in employing "AI" to improve their existing systems or workflows. This rise is especially present in industrial settings, e.g. manufacturing, where—in the past—the usage of AI has usually been limited due to various challenges surrounding the gathering of data. However, there have been concentrated efforts to automate machinery that come with an increase in usable data, which—paired with the wish of some stakeholders to automate through "AI"—makes new applications of AI available. Nevertheless, many stakeholders, especially those that will interact with the system on a daily basis, will not sufficiently trust AI models, hindering their adoption. This issue can be alleviated by using explainable models that can create trust through their transparency, rather than solely through statistical evaluations.

In this extended abstract, past work on how to determine specific requirements of various stakeholder groups on the model structure is reintroduced and one result from a real-world case study is discussed. Additionally, an approach to design a Learning Classifier System that delivers such models is highlighted.

Conventional classifier systems in evolutionary machine learning often struggle to detect and generalize hierarchical patterns, and thus cannot solve complex real-world problems. Inspired by lateralization and modularity in the biological brain, this extended abstract introduces the developed lateralized classifier system that evolves modular abstractions by decomposing problems into reusable knowledge components. The lateralized system consists of two complementary modules: one (Left Hemispheric Stratagem Module) focused on learning fine-grained, constituent patterns and the other (Right Hemispheric Stratagem Module) on extracting abstract, holistic relationships. Knowledge is encoded using disjunctive normal form-based code fragments and organized into hierarchical concepts stored in a heterogeneous knowledge pool. This structure enables recursive composition and facilitates efficient transfer of building-block knowledge across tasks. Experimental evaluations using complex Boolean problems demonstrate that the lateralized system significantly outperforms conventional learning classifier systems for solving complex problems such as the n-bit Parity problems and 18-bit hierarchical multiplexer problems.

The Adam optimiser is a popular adaptive gradient-based learning algorithm used to train neural networks. Adam uses adaptive learning rates (estimated per neural network weight) to increase the speed of convergence. While Adam is claimed to automatically perform step size annealing, it is not immune to oscillations, i.e., updating weights in inconsistent directions. This paper investigates the oscillatory behaviour of Adam in the context of computer vision classification tasks under different batch sizes. We confirm that Adam is indeed highly susceptible to oscillations. Further, we study starting gradient and magnitude of randomly initialised weights as predictors of weight saliency. Conversely to common belief, we discover that the initial gradient is not a reliable predictor of weight saliency. We attribute this finding to inherent oscillations. We also discover that larger batch sizes are more likely to yield inactive weights, bearing important implications for weight pruning.

The application of Large Language Models (LLMs) for Automated Algorithm Discovery (AAD), particularly for optimisation heuristics, is an emerging field of research. This emergence necessitates robust, standardised benchmarking practices to rigorously evaluate the capabilities and limitations of LLM-driven AAD methods and the resulting generated algorithms, especially given the opacity of their design process and known issues with existing benchmarks. To address this need, we introduce BLADE (Benchmark suite for LLM-driven Automated Design and Evolution), a modular and extensible framework specifically designed for benchmarking LLM-driven AAD methods in a continuous black-box optimisation context. BLADE integrates collections of benchmark problems (including MA-BBOB and SBOX-COST among others) with instance generators and textual descriptions aimed at capability-focused testing, such as generalisation, specialisation and information exploitation. It offers flexible experimental setup options, standardised logging for reproducibility and fair comparison, incorporates methods for analysing the AAD process (e.g., Code Evolution Graphs and various visualisation approaches) and facilitates comparison against human-designed baselines through integration with established tools like IOHanalyser and IOHexplainer. BLADE provides an 'out-of-the-box' solution to systematically evaluate LLM-driven AAD approaches. The framework is demonstrated through two distinct use cases exploring mutation prompt strategies and function specialisation.

The recent and rapid progress in large language models (LLMs) has markedly influenced research efforts in the automated design and configuration of metaheuristic algorithms. A common limitation of contemporary LLMs is their finite context window, which constrains the amount of information they can effectively utilize during generation. In this study, we investigate the role of conversational context in the metaheuristic design process. This study explores two distinct aspects of LLM-based metaheuristic design: (1) the effect of conversational context on the performance of generated optimizers, and (2) its influence on the validity of generated code. Both are investigated using the EASE framework. The findings yield several unexpected insights, which are discussed in detail in the paper, offering a deeper understanding of how context affects the reliability and effectiveness of LLM-assisted algorithm generation.

In machine learning, Neural Architecture Search (NAS) requires domain knowledge of model design and a large amount of trial-and-error to achieve promising performance. Meanwhile, evolutionary algorithms have traditionally relied on fixed rules and pre-defined building blocks. The Large Language Model (LLM)-Guided Evolution (GE) framework transformed this approach by incorporating LLMs to directly modify model source code for image classification algorithms on CIFAR data and intelligently guide mutations and crossovers. A key element of LLM-GE is the "Evolution of Thought" (EoT) technique, which establishes feedback loops, allowing LLMs to refine their decisions iteratively based on how previous operations performed. In this study, we perform NAS for object detection by improving LLM-GE to modify the architecture of You Only Look Once (YOLO) models to enhance performance on the KITTI dataset. Our approach intelligently adjusts the design and settings of YOLO to find the optimal algorithms against objective such as detection accuracy and speed. We show that LLM-GE produced variants with significant performance improvements, such as an increase in Mean Average Precision from 92.5% to 94.5%. This result highlights the flexibility and effectiveness of LLM-GE on real-world challenges, offering a novel paradigm for automated machine learning that combines LLM-driven reasoning with evolutionary strategies.

This paper proposes an enhanced approach to Simple Contrastive Graph Clustering (SCGC) by utilizing Cartesian Genetic Programming (CGP) to evolve neural network architectures. The evolutionary algorithm dynamically optimizes the structure and hyperparameters of SCGC, including the number and size of linear layers, optimizer choice (such as Adam, RMSprop, or SGD), learning rates, weight decay, and loss functions, tailored specifically to the given datasets. Using CGP, we automate both the design and training of SCGC architectures, evolving optimized neural networks with minimal manual intervention. Experimental results conducted across multiple generations on ten benchmark datasets demonstrate that our evolved SCGC consistently achieves superior clustering performance compared to state-of-the-art methods, with notable improvements in accuracy, F1-score, and computational efficiency. The evolutionary process not only optimizes the network topology, but also systematically refines hyperparameters, resulting in robust, highly adaptive, and computationally efficient clustering solutions.

In this paper we present a Variational Quantum Algorithm (VQA) for the Permutation Flow Shop Scheduling Problem (PFSSP). This scheduling problem is challenging to be solved with a quantum algorithm because the objective function is quite difficult to be expressed by means of a Hamiltonian, and a large number of qubits is generally required. For this reason, we propose to use a variational approach where the solution is represented with a limited number of qubits and the objective function can be computed without explicitly synthesizing a Hamiltonian operator. The algorithm has been implemented and tested on a simulator; the preliminary experimental results demonstrate that our solution could represent a viable alternative to solve the PFSSP on real quantum devices.

QAOA is a hybrid quantum-classical algorithm to solve optimization problems in gate-based quantum computers. It is based on a variational quantum circuit that can be interpreted as a discretization of the annealing process that quantum annealers follow to find a minimum energy state of a given Hamiltonian. This ensures that QAOA must find an optimal solution for any given optimization problem when the number of layers, p, used in the variational quantum circuit tends to infinity. In practice, the number of layers is usually bounded by a small number. This is a must in current quantum computers of the NISQ era, due to the depth limit of the circuits they can run to avoid problems with decoherence and noise. In this paper, we show mathematical evidence that QAOA requires exponential time to solve linear functions when the number of layers is less than the number of different coefficients of the linear function n. We conjecture that QAOA needs exponential time to find the global optimum of linear functions for any constant value of p, and that the runtime is linear only if p ≥ n. We conclude that we need new quantum algorithms to reach quantum supremacy in quantum optimization.

The Steiner Traveling Salesman Problem (STSP) is a variant of the classical Traveling Salesman Problem. The STSP involves incorporating steiner nodes, which are extra nodes not originally part of the required visit set but that can be added to the route to enhance the overall solution and minimize the total travel cost. Given the NP-hard nature of the STSP, we propose a quantum approach to address it. Specifically, we employ quantum annealing using D-Wave's hardware to explore its potential for solving this problem. To enhance computational feasibility, we develop a preprocessing method that effectively reduces the network size. Our experimental results demonstrate that this reduction technique significantly decreases the problem complexity, making the Quadratic Unconstrained Binary Optimization formulation, the standard input for quantum annealers, better suited for existing quantum hardware. Furthermore, the results highlight the potential of quantum annealing as a promising and innovative approach for solving the STSP.

Customer segmentation is strategic for increasing business revenues, as it allows a company to offer tailored products and services. Companies have access to vast amounts of information to generate customer clusters. In the banking sector, for instance, transactional data may reveal several hidden customer characteristics. The lookalike clustering problem aims to expand an initial cluster by detecting similar customers from a larger set. In this work, we model the lookalike clustering problem as a Quadratic Unconstrained Binary Optimization problem and solve it by means of a Quantum Annealer (QA). We tune the penalization parameters of the QA through an adaptive subgradient-inspired algorithm. We then compare QA performance against those obtained by a Simulated Annealing and the classical integer programming solver Gurobi. The results show that QA, when properly tuned, is able to find the optimal solution for small instances of the problem, even when the number of variables exceeds the number of couplers of QA. It fails, instead, for larger ones, due to the high level of noise. The results also show that QA requires a very short time to generate feasible and optimal solutions compared to classical algorithms, thus proving its potential for the solution of fully connected graphs.

Surrogate-assisted evolutionary algorithms often implement regression models to approximate fitness values. In this paper we introduce new approaches using classification-based surrogate models that predict relative solution quality rather than exact fitness values. We introduce three classification-based surrogate models to improve the efficiency of JADE which is one of the best-performing optimization methods. We demonstrate the efficiency of the considered classification-based surrogate model assisted JADE by benchmarking on the CEC 2013 benchmark suite and comparing the results with the regression-based surrogate model, and with the plain JADE.

Recent work on expensive single-objective Computational Fluid Dynamics (CFD) design problems has identified a trio of surrogate assisted evolutionary algorithms (LSADE, TS-DDEO and ESA) as being particularly strong performers. In this work, we contrast their performance on multi-objective optimisation, embedding eight different popular scalarising functions within their pipelines to facilitate their use in this domain, and compare them to a Bayesian optimisation (BO) alternative on a range of synthetic problems. We find that for bi-objective problems ESA and TS-DDEO outperform the others, but as the number of objectives increases the ranking reverses and BO becomes the dominant performer. Interestingly, the older scalarisation approaches tend to perform better in the configurations used, though again performance is seen to vary as the number of objectives increases. We introduce a new real world multi-objective CFD design benchmark, that of optimising a sand trap geometry, and observe that performance on the collection of synthetic test functions is not predictive on this practical problem — highlighting the need to add such practical test cases to the benchmarking suits in this domain.

Mesh deformation plays a critical role in aerodynamic shape optimisation, enabling efficient geometry modifications without requiring full mesh regeneration. This paper presents a Radial Basis Function (RBF) mesh deformation method, offering a computationally efficient and robust alternative to traditional approaches. The method is integrated into a gradient-free optimisation framework using Bayesian Optimisation (BO), and tested on three case studies. Results show the technique accommodates large shape changes while preserving mesh quality and reducing numerical noise.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder of the nervous system with a high rate of misdiagnosis. In this paper, we propose a Grammatical Evolution (GE) approach for classifying PD patients, that uses hinge loss as a surrogate fitness function. We compare our approach to standard GE using accuracy as its fitness function. Our results demonstrate that the surrogate fitness approach consistently produces models with better accuracy and reduced complexity. These results highlight the potential of using our approach as a powerful tool for developing trustworthy AI applications in the medical domain.

Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show that no single algorithm dominates across all datasets. We propose a call for action from SR community in maintaining and evolving SRBench as a living benchmark that reflects the state-of-the-art in symbolic regression, by standardizing hyperparameter tuning, execution constraints, and computational resource allocation. We also propose deprecation criteria to maintain the benchmark's relevance and discuss best practices for improving SR algorithms, such as adaptive hyperparameter tuning and energy-efficient implementations.

We introduce Hierarchical Multi-view Symbolic Regression (H-MvSR), a novel evolutionary framework that discovers closed-form expressions linking environmental drivers to functional gene abundances across multiple ocean layers. Our method models each depth layer as a separate view while enforcing hierarchical constraints that promote both global structural consistency and local biological specificity. The symbolic models are evolved under multi-objective criteria that balance predictive accuracy, expression simplicity, and cross-view coherence. Furthermore, the framework incorporates pathway-level biological priors, enabling grouped symbolic modeling of molecular functions participating in the same metabolic process. We evaluate H-MvSR using depth-resolved genomic and environmental data from global ocean expeditions and compare its performance against state-of-the-art SR systems. Results show that H-MvSR improves interpretability and multiview consistency while recovering biologically meaningful relationships.

Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR) using genetic programming (GP) can generate flexible models but is prone to unreliable behaviour in extrapolation. This paper investigates whether adding synthetic data can help improve performance in such cases. We apply Kernel Density Estimation (KDE) to identify regions in the input space where the training data is sparse. Synthetic data is then generated in those regions using a knowledge distillation approach: a teacher model generates predictions on new input points, which are then used to train a student model. We evaluate this method across six benchmark datasets, using neural networks (NN), random forests (RF), and GP both as teacher models (to generate synthetic data) and as student models (trained on the augmented data). Results show that GP models benefit most when trained with synthetic data from NN and RF. The most significant improvements are observed in extrapolation regions, while changes in interpolation areas show only slight changes. We also observe heterogeneous errors, where model performance varies across different regions of the input space. Overall, this approach offers a practical solution for better extrapolation.

System identification is the task of automatically learning the model of a dynamical system, which can be represented as a system of ordinary differential equations (ODEs), using data points from time-series trajectories. This challenge has been addressed through various methods, including sparse regression, specialized neural networks, and symbolic regression. However, applying standard symbolic regression requires transforming the trajectory data to frame the problem as learning a set of regular equations. This study presents a first comprehensive comparison of the two most common data transformation approaches for system identification, evaluating their performance on a recently published benchmark suite of ODE systems. Our findings reveal that both approaches are highly sensitive to even moderate amounts of added noise, to different degrees. More surprisingly, we also show that data transformations can generate misleading search spaces, even under noise-free conditions. Further analysis indicates that reducing the data sampling step size significantly improves performance, suggesting that both transformation techniques are also affected by sampling frequency, and indicating possible future directions of research for system identification using symbolic regression.

Vehicle Routing Problems are critically important in areas such as logistics, waste collection, and emergency service distribution. Traditionally, designing effective algorithms for these problems requires considerable manual effort and expert knowledge. In this paper, we apply the automatic algorithm design approach to vehicle routing problems using the EMILI (Easily Modifiable Iterated Local Search) framework. EMILI enables a modular strategy for algorithm development by combining various algorithmic components through context-free grammars. When coupled with an automatic algorithm configurator, design decisions can be translated into configurable parameters. Experimental results on classical vehicle routing instances show that the automatically generated algorithms not only achieve competitive solution quality but also outperform state-of-the-art algorithms with significantly less design effort.

Given the rapid advancement of AI across various fields, it is crucial to bridge computational methods and creative poetry generation, pushing the boundaries of what artificial intelligence can achieve in creative domains. This work explores the potential of blending the NSGA-II evolutionary optimization, Gemini 1.5 and Monte Carlo Tree Search to generate and iteratively enhance poems while also incorporating human feedback. Since natural language generation can transform non linguistic inputs like data into expressive text, it opens up avenues to explore innovative poetic form. The results demonstrate the model's better poem generation over traditional methods, showcasing its ability to refine poetic quality dynamically through evolutionary cycles and user feedback, thus setting a new standard for automated poetic creativity. Future work will explore deeper semantic control mechanisms, refined evaluation strategies, and broader linguistic adaptability across diverse poetic traditions.

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for high-dimensional expensive multi-objective optimization problems (EMOPs). However, they often suffer from the curse of dimensionality, inefficiency, and poor adaptability. Hence, we proposes an efficient pretrained model-based SAEA, named Prior-data Fitted Networks-evolutionary algorithm (PFN-EA). By leveraging a pretrained baseline model instead of computationally expensive Gaussian Process (GP) models, PFN-EA significantly reduces training time complexity from O(n³) to O(n²d). It enhances accuracy through domain-adaptive fine-tuning that incorporates cross-domain prior knowledge, while balancing evaluation costs by alternating between real and surrogate-based predictions. Additionally, it combines reference vector-guided selection with dynamic dimensionality reduction for diversity-convergence trade-offs. Experiments on 100- to 200-dimensional DTLZ benchmarks show that PFN-EA outperforms six state-of-the-art SAEAs, achieving top results in 8 out of 14 cases with O(nd) prediction complexity. This work provides a scalable solution for high-dimensional EMOPs and pioneers the use of pretrained models in SAEAs.

Precision livestock farming increasingly relies on data-driven models to improve animal welfare and farm management. However, most existing approaches require manual data processing and result in black-box models that lack transparency for end-users such as veterinarians and farm staff. In this work, we propose an interpretable AutoML framework based on evolutionary computing to address these challenges. At the core of our system is an evolutionary preprocessing pipeline that automatically processes heterogeneous livestock data, including time series and static farm records, to generate compact and meaningful feature sets. To evaluate the pipeline's output, we employ symbolic regression to predict the remaining time to farrowing in sows, a key task to optimize confinement periods and improve welfare. Our approach focuses on producing interpretable models that balance predictive accuracy and simplicity. Initial results demonstrate that evolutionary strategies can successfully identify relevant feature transformations, enabling accurate and understandable farrowing predictions. This study highlights the potential of combining evolutionary preprocessing and symbolic regression to create trustworthy artificial intelligence tools for livestock farming. Future work will extend the pipeline to additional data types and further validate its usability in farm environments.

Designing quantum circuits is a major challenge due to their sensitivity to noise and the complexity of quantum logic. This paper presents an evolutionary algorithm that balances multiple design criteria, namely fidelity and circuit depth, by combining them into a single scalarised fitness function. This approach enables efficient optimisation while avoiding the computational cost of fully multi-objective methods. A novel circuit representation and set of noise-aware genetic operators are introduced, and the technique is applied to evolve implementations of the Quantum Fourier Transform (QFT) for two- and three-qubit systems. Experimental results show that evolved circuits match or exceed ideal fidelity and outperform textbook QFT implementations under simulated noise. These findings highlight the potential of hardware-aware evolutionary design for producing circuits that are both accurate and resilient on near-term quantum hardware.