Planned Program Tracks
Ant Colony Optimization and Swarm Intelligence:
Swarm Intelligence (SI) deals with natural and artificial systems composed of a large number of individuals that generate collective behaviors using decentralized control and self-organization. These behaviors are a result of the local interactions of the individuals with each other and with their environment. SI algorithms are inspired by the behavior of social insects such as ants, bees, and wasps, as well as by that of other animal societies such as flocks of birds, or fish schools. Two popular swarm intelligence techniques for optimization are ant colony optimization (ACO) and particle swarm optimization (PSO). Other examples of SI techniques include honey bee optimization, bacterial foraging, firefly optimization, and algorithms inspired by ants' cemetery building behavior, or dynamic task allocation algorithms inspired by wasp colonies behaviour.
The ACOP/SI track invites sbmissions of original and unpublished work in the following areas of research:
* Applications of ACO, PSO, and other SI algorithms to real-world problems and games
* SI techniques for difficult optimization problems, including:
- multi-objective optimization,
- dynamic and uncertain environments,
- dynamic multi-objective optimization,
- finding multiple solutions,
- tracking multiple solutions in dynamic environments,
- dynamic constraints
* New computational models and techniques based on SI
* New hybrids between these algorithms and other methods
* Biological foundations
* Models of the behavior of natural and artificial SI systems
* Theoretical analysis of SI algorithms
* Benchmarking SI algorithms and new empirical results
* Multiswarm methods, adaptation and self adaptation techniques
Digital Entertainment Technologies and Arts:
The arts, music and games are key application fields for genetic and evolutionary computation and related techniques. This track explicitly focusses on these areas, thereby strengthening a domain of high scientific, commercial, and cultural relevance. We are especially interested in seeing works that touch on different aspects of this domain.
The track invites submissions that present original work on the use of evolutionary computational techniques and related algorithms in games, music and the creative arts, be they of methodological, experimental or theoretical nature. Topics of interest include, but are not limited to:
- Creative virtual ecosystems
- Evolutionary arts
- Artificial creative agents
- Interactive and automated evolutionary algorithms for creative applications
Interactive environments and games:
- Virtual worlds
- Automated content generation
- Game AI
- Educational game design
- Intelligent interactive narratives
- Learning and adaptation in games
- Player satisfaction
- Composition and sound synthesis
- Recognition and classification
Analysis of Computational Intelligence techniques for games, music and the arts
He is Research Associate at the Computer Science Department, TU Dortmund, Germany, where he also received his Diploma degree in 1998.
His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective niching and the experimental methodology for (non-deterministic) optimization algorithms. He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games, pushing forward modern approaches of experimental analysis as the Exploratory Landscape Analysis (ELA) and innovative uses of surrogate models. He was involved in founding the EvoGames track at Evo* and the Digital Entertainment Technologies and Arts (DETA) track at GECCO.
He is is an academic at Monash University where he is an Artificial Life researcher and generative artist working in electronic media. His interests include ecosystem simulation and agent-based modelling, artificial chemistry, self-assembling systems, the evolution of complexity, the history and philosophy of science and art, and the links that bind all fields together. His interactive software, animations, musical and other artistic works typically employ biologically-inspired generative systems and have been exhibited or performed internationally at museums, galleries and in science museums. Alan has a PhD in Computer Science (Monash University 1999), a Postgrad. Dip. in Animation and Interactive Media (RMIT 1995), Honours in Comp. Sci. (Monash University 1992) and a B.Sc. (App. Maths 1991).
Evolutionary Combinatorial Optimization and Metaheuristics:
The aim of this track is to provide a forum for high quality research on metaheuristics for combinatorial optimization problems. Plenty of hard problems in a huge variety of areas, including logistics, network design, bioinformatics, engineering, business, etc., have been tackled successfully with metaheuristic approaches. For many problems the resulting algorithms are considered to be state-of-the-art. Apart from evolutionary algorithms, the class of metaheuristics includes prominent members such as tabu search, iterated local search, variable neighborhood search, memetic algorithms, simulated annealing, GRASP, ant colony optimization and others.
Scope: Submission concerning applications or the theory of all kinds of metaheuristics for combinatorial optimization problems are encouraged. Topics include (but are not limited to):
1. Applications of metaheuristics to combinatorial optimization problems
2. Theoretical developments in combinatorial optimization and metaheuristics
3. Representation techniques
4. Neighborhoods and efficient algorithms for searching them
5. Variation operators for stochastic search methods
6. Search space analysis
7. Comparisons between different (also exact) techniques
8. Constraint-handling techniques
9. Hybrid methods, Adaptive hybridization techniques and Memetic Computing Methodologies
10. Insight into problem characteristics of problem classes
local search, variable neighborhood search, iterated local search, tabu search, simulated annealing, very large scale neighborhood search, search space analysis, hybrid metaheuristic, matheuristic, memetic algorithm, ant colony optimization, particle swarm optimization, scatter search, path relinking, GRASP, vehicle routing, cutting and packing, scheduling, timetabling, bioinformatics, transport optimization, routing, network design, representations
Yew Soon Ong
He is currently an Associate Professor with the School of Computer Engineering, Nanyang Technological University, Singapore. He is also Director of the Center for Computational Intelligence. His current research interest lies in Nature-Inspired Computing & Design that spans across Evolutionary & Memetic computation, Robust & Complex Design, Stochastic Optimization, Informatics, and cloud computing. He is 1) co-founder editor-in-chief of Memetic Computing Journal 2) co-founder chief editor of Book Series on Studies in Adaptation, Learning, and Optimization, 3) associate editor of IEEE Computational Intelligence Magazine, 4) associate editor of IEEE Transactions on Systems, Man and Cybernetics - Part B, 5) member of editorial board in (i) Soft Computing Journal, (ii) International Journal of Computational Intelligence, 6) guest editor of i) IEEE Transactions on Evolutionary Computation, ii) IEEE Transactions on Systems, Man and Cybernetics - Part B, iii) Journal of genetic Programming and Evolvable Machine and iv) Soft Computing Journal. Besides serving as editors of special issues dedicating to research on Memetic Computing, and Evolutionary Computation in Dynamic and Uncertain Environments in high-quality journals, he has also co-edited volumes on Advances in Natural Computation, and Evolutionary Computation published by Springer Verlag. He is also founder and chair of the Task Force on Memetic Computing in the IEEE Computational Intelligence Society Emergent Technology Technical Committee. He has coauthored over 100 refereed publications comprising of 42 refereed journals, 66 refereed conference papers and 5 book chapters, excluding 5 edited books, 3 edited special issues and 2 patents filed. More information can be found at http://www.ntu.edu.sg/home/asysong
received the doctoral degree in 2004 from the Free University of Brussels (Belgium). He currently holds a temporal Associate Professor position at the Universitat Politècnica de Catalunya in Barcelona (Spain). Current subject of his research is the use of swarm intelligence techniques for the management of large-scale mobile ad-hoc and sensor networks, as well as the hybridization of metaheuristics with more classical artificial intelligence and operations research methods.
He is a member of the editorial boards of international journals such as Computers & Operations Research and Swarm Intelligence. As a co-founder, he initiated the series of International Workshops on Hybrid Metaheuristics (HM). He presented invited tutorials at conferences such as PPSN, GECCO, and HIS, and keynote talks at international conferences such as ANTS 2008 and BIOMA 2010.
Evolutionary Multiobjective Optimization:
Multiobjective optimization problems (MOPs) arise frequently in applications. They have several (two or more), normally conflicting, objectives that have to be satisfied at the same time. The Evolutionary Multiobjective Optimization (EMO) track call for contributions describing the use of a range of metaheuristic methodologies (mainly but not limited to evolutionary algorithms) to solve MOPs, aiming to find good trade-off (or compromise) solutions.
The EMO track at GECCO aims to bring together both experts and newcomers working on this area to discuss different issues including (but not limited to):
1. Real-world applications in engineering, business, computer science, biological sciences, scientific computation, etc.
2. New multi-objective optimization algorithms based on metaheuristics such as: genetic algorithms, evolution strategies, scatter search, genetic programming, evolutionary programming, artificial immune systems, particle swarm optimization, ant colony optimization, etc.
3. Performance measures for EMO
4. Test functions and comparative studies of algorithms for EMO
5. Techniques to maintain diversity in an EMO context
6. Theoretical investigations of EMO
7. Dimensionality analysis (e.g., techniques to deal with a high number of objectives and/or decision variables)
8. Parallelization of EMO techniques
9. Hybrid approaches (e.g., combinations with mathematical programming techniques)
10. Local search in an EMO context (e.g., memetic algorithms for multiobjective optimization)
11. Multiobjective combinatorial optimization
12. Incorporation of preferences into EMO algorithms
13. Handling uncertainty and noise in an EMO context
14. Dynamic multiobjective optimization using EMO algorithms
15. Special representations and operators for EMO algorithms
16. Software architectures for development of EMO algorithms
17. Learning and intelligent mechanisms for EMO
18. Multi-level optimization using EMO algorithms
19. Many-objective optimization using EMO algorithms
20. Multi-criteria decision making and EMO techniques
applications of evolutionary multi-objective optimization,
dynamic multi-objective optimization,
expensive evaluation functions,
interactive multi-objective methods,
machine learning in multi-objective optimization,
multi-level optimization problems,
multi-objecitve evolutionary strategies,
multi-objecitve problem decomposition,
multi-objective algorithms comparison,
multi-objective ant colony optimization,
multi-objective artificial immune systems,
multi-objective combinatorial problems,
multi-objective constraint satisfaction,
multi-objective differential evolution,
multi-objective evolutionary local search,
multi-objective evolutionary programming,
multi-objective genetic algorithms,
multi-objective genetic programming,
multi-objective hybrid algorithms,
multi-objective memetic algorithms,
multi-objective online optimization,
multi-objective particle swarm optimization,
multi-objective performance metrics,
multi-objective preferences management,
multi-objective robust optimization,
multi-objective solution encodings,
multi-objective test functions,
multiple criteria techniques,
niching, elitism and diversity techniques,
parallel multi-objective algorithms,
theoretical studies in multi-objective optimization,
uncertainty and noise multi-objective optimzation
In the field of Genetic Programming (GP), evolutionary algorithms are used to automatically search for an algorithm or structure that solves a given problem. Various representations have been used in GP, such as tree-structures, linear sequences of code, graphs or grammars. The Genetic Programming (GP) track invites original submissions on all aspects of the evolutionary generation of computer programs or other variable sized structures for specified tasks. Topics include but are not limited to:
1. Theoretical developments
2. Empirical studies of GP performance and behavior
3. Novel algorithms, representations and/or operators
4. Hybrid architectures including GP components
5. Unconventional evolvable computation
6. Evolution of tree or graph structures
7. Evolution of Lindenmayer Systems
8. Grammar-based GP
9. Linear GP
10. Self-Reproducing Programs
11. Evolution of various classes of automata or machines (e.g. cellular automata, finite state machines, pushdown automata, Turing machines)
12. Object-oriented Genetic Programming
13. Evolution of functional languages
classification, planning, evolutionary theory, parallelization, computer vision, animation, functional gp, financial application, GPU, dynamic systems, gp fitness landscape, symbolic regression, cgp and its applications, developmental gp and financial applications, modular gp and analysis, bloat, SAT, local search, grammatical evolution, coevolution, hierarchical gp, evolvability, applications (robot, financial, gene chip etc), constraints, grammar, data mining, visualization, art.
Genetics-Based Machine Learning:
The Genetics-Based Machine Learning (GBML) track at GECCO covers all
advances in theory and application of evolutionary computation methods
to Machine Learning (ML) problems.
ML presents an array of paradigms -- unsupervised, semi-supervised,
supervised, and reinforcement learning -- which frame a wide range of
clustering, classification, regression, prediction and control tasks.
Evolutionary methods have a range of uses in ML:
- addressing subproblems of ML e.g.
- feature selection and construction
- optimising parameters of other ML methods
- as learning methods e.g.
- generating classification hypotheses with Genetic Programming
- learning control systems or cognitive modelling with Learning Classifier Systems
- as meta-learners which adapt base learners e.g.
- evolving the structure and weights of neural networks
- evolving the data base and rule base in genetic fuzzy systems
- evolving ensembles of base learners
- evolving representations, update rules or algorithms for base
The global search performed by evolutionary methods can complement the
local search of non-evolutionary methods and combinations of the two
are particularly welcome.
Some of the main GBML subfields are:
- Learning Classifier Systems (LCS) are rule-based systems introduced
by John Holland in the 1970s. LCSs are one of the most active and
best-developed forms of GBML and we welcome all work on them.
- Genetic Programming (GP) when applied to machine learning tasks (as
opposed to function optimisation).
- Evolutionary ensembles, in which evolution generates a set of
learners which jointly solve problems.
- Artificial Immune Systems (AIS).
- Evolving neural networks or Neuroevolution.
- Genetic Fuzzy Systems (GFS) which combine evolution and fuzzy logic.
In addition we encourage submissions including but not limited to the
1. Theoretical advances
- Theoretical analysis of mechanisms and systems
- Identification and modeling of learning and scalability bounds
- Connections and combinations with machine learning theory
- Analysis and robustness in stochastic, noisy, or non-stationary environments
- Complexity analysis in MDP and POMDP problems
- Efficient algorithms
2. Modification of algorithms and new algorithms
- Evolutionary rule learning, including but not limited to:
- Michigan style (SCS, NewBoole, EpiCS, ZCS, XCS, UCS...)
- Pittsburgh style (GABIL, GIL, COGIN, REGAL, GA-Miner, GALE, MOLCS, GAssist...)
- Anticipatory LCS (ACS, ACS2, XACS, YACS, MACS...)
- Iterative Rule Learning Approach (SIA, HIDER, NAX, BioHEL, ...)
- Artificial Immune Systems
- Genetic fuzzy systems
- Learning using evolutionary Estimation of Distribution Algorithms (EDAs)
- Evolution of Neural Networks
- Evolution of ensemble systems
- Other hybrids combining evolutionary techniques with other machine learning techniques
3. Issues in GBML
- Competent operator design and implementation
- Encapsulation and niching techniques
- Hierarchical architectures
- Default hierarchies
- Knowledge representations, extraction and inference
- Data sampling
- (Sub-)Structure (building block) identification and linkage learning
- Integration of other machine learning techniques
- Data mining
- Large scale datasets
- Bioinformatics and life sciences
- Rapid application development frameworks for GBML
- Robotics, engineering, hardware/software design, and control
- Cognitive systems and cognitive modeling
- Dynamic environments, time series and sequence learning
- Artificial Life
- Adaptive behavior
- Economic modelling
- Network security
- Other kinds of real-world applications
5. Related Activities
- Visualisation of performance
- Platforms for GBML, e.g. GPGPUs
- Competitive performance, e.g. GBML performance in Competitions and Awards
- Education and dissemination of GBML, e.g. software for teaching and exploring aspects of GBML.
Browne's thesis for an Engineering Doctorate regarded the industrial development of a Learning Classifier System for the Data Mining of quality control within a Steel Mill. A
post-doctorate appointment from 1998 to 2001 was in the Control and Instrumentation Research Group, University of Leicester, UK. From October 2001 to August 2009 he lectured in
the Cybernetic Intelligence Research Group, University of Reading. He was appointed to Senior Lecturer, Victoria University of Wellington, NZ, in September 2009. His main area of
research is Applied Cognitive Systems - using inspiration from natural intelligence to enable computers/machines/robots to exhibit useful behaviours. This includes Learning
Classifier Systems, modern heuristics for industrial application and Cognitive Robotics. Conferences / Workshops Organisation has included COGRIC 2006: Cognitive Robotics and
Control, EPSRC/NSF sponsored workshop that brought together internationally leading figures in order to discuss latest advancements and direct future research. He has served on
the organising committee of International Workshop on Learning Classifier Systems for 2009 and 2010.
I am a senior lecturer in computer science at the university of Bristol,
where I am director of the MSc in machine learning, data mining and high
performance computing. I hold a BA in psychology, and an MSc and PhD in
computer science. Most of my work involves the analysis of complex
adaptive systems. I have written about learning classifier systems,
artificial immune systems, ensembles, methodological issues in machine
learning, the complexity of learning, network intrusion detection, AI for
game playing, the design of software for reinforcement learning, social
insect biology, and education. I co-chaired AISB 2006, IWLCS 2005 and
2006, was a GECCO 2007 track chair and an associate editor of the IEEE
Transactions on Evolutionary Computation from 2004-10.
Artificial Intelligence, Artificial Life, Cognitive Modelling,
Genetics-based Machine Learning, Evolutionary Rule Learning, Learning
Classifier Systems, Michigan style LCS, Pittsburgh style LCS,
Anticipatory LCS, Artificial Immune Systems, Iterative Rule Learning,
Genetics-based Inductive Learning, Genetic Fuzzy Systems, Learning
using Evolutionary Estimation of Distribution Algorithms, Evolution of
Ensemble Systems, Neuroevolution technologies, Evolutionary concepts
within Machine Learning, Applications
Parallel Evolutionary Systems :
The future of computing is parallel, as it seems that the current trend in CPU development is: slower cores on the same chip. This observation is quite pessimistic for sequential algorithms, but luckily enough, evolutionary algorithms are inherently parallel!
This GECCO track aims at developing the cross-fertilization of knowledge between evolutionary algorithms (meta-heuristics in general) and parallelism. Working in two domains of research is both difficult and fruitful. Knowledge on parallelism and networking helps in creating parallel algorithms for clusters or grids of computers. However, it is also necessary to develop proper benchmarks, software tools, and metrics to measure the behavior of algorithms in a meaningful way. A conceptual separation between physical parallelism and decentralized algorithms is needed to better analyze the resulting algorithms.
This track expects high quality papers on contributions to the theory and the application of techniques born from the crossover of the traditional parallel field and meta-heuristics. Submissions providing significant contributions to problem solving (efficiency and also accuracy) while being methodologically well-founded are also welcome.
This track includes (but is not limited to) topics concerning design, implementation, and application of parallel evolutionary algorithms and meta-heuristics (GA, ES, EP, GP, ACO, PSO, SA, EDAs, TS, etc). As an indication, contributions are welcomed in the following areas:
- Parallel evolutionary algorithms and meta-heuristics
- Master/slave models
- Massively parallel algorithms
- SIMD/MIMD and FPGA parallelization
- Distributed and shared memory parallel algorithms
- Parallel algorithms on multi-core machines and clusters of machines
- Graphics Processing Units in optimization
- Parallel hybrid/memetic algorithms
- Grid computing
- Cloud computing
- Peer to peer (P2P) algorithms
- Ad-hoc and mobile networks for parallel algorithms
- Theory on decentralized and parallel algorithms
- Parallel software frameworks/libraries
- Parallel test benchmarks
- Algorithms and tools for helping in designing new parallel algorithms
- Statistical assessment of performance for parallel algorithms
- Real-world applications in data mining, bioinformatics, engineering, and telecommunications, etc.
The GECCO 2012 theory track welcomes all papers that address theoretical issues in the whole of evolutionary computation and allied sciences. So, in addition to Genetic Algorithms, Evolutionary Strategies, Genetic Programming and other traditional EC areas, we also very much welcome theory papers in Artificial Life, Ant Colony Optimization, Swarm Intelligence, Bioinformatics, Computational Biology, Estimation of Distribution Algorithms, Generative and Developmental Systems, Genetics-Based Machine Learning, Search Based Software Engineering, and more. More information can be found at http://www.mpi-inf.mpg.de/~tfried/GECCO12/
ALIFE track promotes evolutionary computation and bio-inspired heuristics as instruments able to face engineering problems and scientific questions in different areas that include (but are not limited to): artificial life, robotics, and evolvable hardware.
Artificial life studies artificial systems (software, hardware, or
chemical) with properties similar to those of living systems. There are two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties of living systems, such as adaptability, evolvability, active perception, communication, organization.
Evolutionary computation techniques can be particularly useful for a large branch of robotics. The evolution of controllers, morphologies, sensors, and communication protocols is being used to build systems to provide robust, adaptive and scalable solutions to different problems in robotics. This track welcomes contributions addressing problems from control to morphology, from single robot to swarm of robotic units. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.
The term evolvable hardware denotes both the design of electronic devices able to evolve themselves, and the exploitation of evolutionary techniques for creating hardware. While the first task is quite ambitious, the second is routinely applied by industries.
Contributions in this area are expected to show either real and potential applications.
Biological and Biomedical Applications:
Computers have long been applied to biology and biomedical applications but the advent of
genetic and evolutionary computation has dramatically increased interest and activity in the
field. The aim of this GECCO track is to provide a focus for the use of genetic and evolutionary
computation to the biological and biomedical sciences.
Submissions are welcome in the following and related areas:
- Biological systems
- Biomedical systems
- Data analysis
- Data mining
- Genetic networks
- Microarray Data Analysis
- Sequence analysis
- Visualization and imaging
Estimation of distribution algorithms :
Estimation of distribution algorithms (EDAs) are based on the explicit use of probability distributions. They replace traditional variation operators of evolutionary algorithms, such as mutation and crossover, by building a probabilistic model of promising solutions and sampling the built model to generate new candidate solutions. Using probabilistic models for exploration in evolutionary algorithms enables the use of advanced methods of machine learning and statistics for automated identification and exploitation of problem regularities for broad classes of problems. In addition they provide natural ways to introduce problem information into the search by means of the probabilistic model and also to get information about the problem that is being optimized. EDAs provide a robust and scalable solution to many important classes of optimization problems with only little problem specific knowledge.
The aim of the track is to attract the latest high quality research on EDAs in particular, and the use of explicit probabilistic and graphical models in evolutionary algorithms in general. We encourage the submission of original and previously unpublished work especially in the following areas:
1. Advances in the theoretical foundations of EDAs.
2. Novel applications for EDAs.
3. Case studies or showcases that highlight the use of EDA in practical decision making.
4. Interfaces between EDA, Ant Colony Optimization, Evolution Strategies, Cross-Entropy Method and other related methods.
5. Position papers on EDA-related topics.
6. Reviews of specific EDA-related aspects.
7. Comparisons of EDA and other metaheuristics, evolutionary algorithms, more traditional optimization methods of operations research or hybrids thereof.
8. EDA for dynamic, multiobjective or noisy problems and interactive EDA.
9. Hybrid EDAs.
10. New EDAs.
11. Statistical modeling in evolutionary algorithms.
The above list of topics is not exhaustive; if you think that your work does not fit the above categories but the work should belong to the EDA track, please contact the track chairs to discuss this issue.
He received an M.Sc. degree in mathematics and an M.Sc. degree in computer science from the University of the Basque Country, Spain, in 1991 and 1992 respectively, and the PhD degree in computer science from the University of the Basque Country, Spain, in 1998. Since 2008 he is full professor of the University of the Basque Country, Spain where he leads the Intelligent System Group. He is the coauthor of more than 50 ISI journal publications and co-editor of the first book published about Estimation of Distribution Algorithms. His major research interests include machine learning, probabilistic graphical models, evolutionary computation, data mining, metaheuristic algorithms, and real-world applications. Prof. Lozano is associate editor of IEEE trans. on Evolutionary Computation and member of the editorial board of Evolutionary Computation journal, Soft Computing, Applied Intelligence and other three journals.
Pelikan received Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002. He is now an associate professor at the Department of Mathematics and Computer Science at the University of Missouri in St. Louis. In 2006 Pelikan founded the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). Prior to joining the University of Missouri, he worked at the Illinois Genetic Algorithms Laboratory (IlliGAL), the German National Center for Information Technology in Sankt Augustin, the Slovak University of Technology in Bratislava, and the Swiss Federal Institute of Technology (ETH) in Zurich. Pelikan has worked as a researcher in genetic and evolutionary computation since 1995. His most important contributions to genetic and evolutionary computation are the Bayesian optimization algorithm (BOA), the hierarchical BOA (hBOA), the scalability theory for BOA and hBOA, and the efficiency enhancement techniques for BOA and hBOA.
Evolution strategies and evolutionary programming:
Evolution strategies (ES) and evolutionary programming (EP) are
nature-inspired optimization paradigms that generally operate on the
"natural" problem representation (i.e., without a non-trivial
genotype-phenotype mapping). For example, when used in connection with
real-valued problems, ES and EP use real-valued representations of search
points. Moreover, both often rely on sophisticated mechanisms for the
adaptation of their strategy parameters. ES and EP owe much of their
success to their universal applicability, ease of use, and robustness.
This track invites submissions that present original work on ES/EP and
related algorithms that may include, but is not limited to, theoretical
and empirical evaluations, further development and improvement of the
algorithms, and applications to benchmark problems and test function
suites. Particularly encouraged are submissions with focus on
- Adaptation mechanisms
- Interesting ES/EP applications
- ES/EP theory
- Comparisons of ES/EP with other optimization methods
- Related algorithms, such as differential evolution or the cross-entropy method
- Hybrid strategies, such as surrogate methods
- ES/EP in uncertain and/or changing environments
- ES/EP on constrained and/or multimodal problems
- ES/EP for multi-objective optimization
Generative & Developmental Systems:
We are thrilled to announce that Stuart Kauffman has accepted to give a keynote speech for the GDS track. Dr. Kauffman is a renowned theoretical biologist and complex systems researcher especially interested in the question of the origins of life on Earth. He is best known for arguing that the complexity of biological systems and organisms might result as much from self-organization and far-from-equilibrium dynamics as from Darwinian natural selection, as well as for applying models of Boolean networks to simplified genetic circuits. Stuart Kauffman joined in 2010 the University of Vermont where he continues his work with UVM's Complex Systems Center.
Faced with an escalation of systems’ size, the tradition of rigidly designing systems in every detail from a top-down perspective becomes unsustainable. One way of thinking of this problem is the challenge of creating systems with improved scalability. With this direction, much of the focus of the GDS communities has been in developing scalable representations, whether they be artificial developmental systems inspired by biology, or procedural representations drawing from the fields of grammars, programming languages and engineering design.
Another way of thinking of them is as complex systems, defined as large sets of elements that self-organize in a bottom-up fashion through local interactions. Evolutionary computation (EC) still heavily relies on “direct” genotype-phenotype mappings despite the fact that real genes encode the behavior of cells, not organisms. For Evolutionary Development or “evo-devo”, a new domain of biology comparing the morphogenesis of species, the genotype-phenotype link cannot remain an abstraction if we want to break the glass ceiling of evolutionary novelty. From what material exactly do new and functional complex structures spontaneously arise?
Likewise, looking at the full evo-devo picture should be an important concern of systems engineering and computer science when venturing in the arena of autonomous architectures and other self-x properties. The ambition of the GDS track at GECCO 2012 is to contribute to new avenues in evolutionary engineering by stressing the importance of fundamental laws of generative and developmental variations as a prerequisite to the selection of artificial systems. Indirect mappings promote compact encodings, modularity and combinatorial reuse.
As a natural complement to GDS approaches, researchers growing and evolving complex structures must also investigate new selection mechanisms to help them better exploit and manage the potential of such complexity. In that sense, methods that bias the search process toward more discovery and away from convergence, in particular encourage and preserve diversity at both genotypic and phenotypic levels, are an important part of the evo-devo effort. The GDS track is therefore also open to original heuristics, search and optimization algorithms that are especially suited to the emergence of fine-grained, collective entities.
We invite all papers related to the evolution of complexity, including in the areas of:
- advanced representations
- artificial development/embryogeny
- collective robotics
- computational biology
- diversity-preservation mechanisms
- engineering design
- gene regulatory networks
- grammar-based/generative/rewriting systems
- indirect mappings, compact encodings
- Lindenmayer systems (L-systems)
- measures of complexity
- morphogenetic engineering
- novelty search
- neural network plasticity
- procedural representations
- theories on scalable design
- topological design/optimization
More information can be found at http://geb.uma.es/gds2012
He is UCSC's Deputy Director of Information Sciences for their division at NASA Ames Research Center. He recieved is Ph.D. in Computer Science from Brandeis University in 2002 for Generative Representations for Evolutionary Design Automation. He evolved the dynamic gait used on the consumer version of Sony's Aibo and also the X-band antennas used on NASA's ST-5 mission. He also developed the Age Layered Population Structure (ALPS) for improving the robustness of stochastic search algorithms, such as EAs.
He is an elected Full Member of CREA, the research center in cognitive science and self-organization at Ecole Polytechnique, Paris, and currently a Visiting Scientist in the Research Group in Biomimetics (GEB) at the University of Malaga, Spain. In 2009 and 2010, he was Director of the Complex Systems Institute, CNRS, Paris, where he also held a Researcher position (2006-2011). Previously, he was a Visiting Assistant Professor in computer science at the University of Nevada, Reno (2004-2006). He obtained his MSc from the Ecole Normale Supérieure, Paris (1987), his PhD from Université Paris 6 (1991) and completed two postdocs at the Ruhr-Universität Bochum, Germany (1991-1994) and CREA (1995-1998). After a segue through the Bay Area's high-tech industry, he came back to academia full-time in 2004. René was General Chair and main organizer of ECAL 2011, the 11th European Conference on Artificial Life, Paris, the founder of the “Morphogenetic Engineering” workshop series, and an Associate Editor of IEEE Transactions on Neural Networks. His research activities address the computational modeling and simulation of complex multi-agent systems, in particular biological and techno-social, which can also inspire novel principles in intelligent systems design. His interest especially focuses on the self-organization of complex, articulated morphologies (e.g. multicellular organisms) from a swarm of heterogeneous agents, through dynamical, developmental, and evolutionary processes.
Real World Applications:
GECCO: Real World Applications welcomes rigorous experimental, computational and/or applied advances in evolutionary computation (EC) in any discipline devoted to the study of real-world problems. The aim is to bring together a rich and diverse set of fields, such as:
Engineering and Technological Sciences, Mathematical Sciences, Numerical and Computational Sciences, Physical Sciences, Cosmological Sciences, Environmental Sciences, Geophysical Sciences, Oceanographic Sciences, Chemical Sciences, Biological Sciences, Atmospheric Sciences, Aerospace Sciences, Social Sciences and Economics; into a single event where the major interest is on applications including but not limited to:
- Papers that describe advances in the field of EC for implementation purposes, including scalability for solution quality, scalability for algorithm complexity, and implementation in industrial packages like Matlab, Mathematica, and R.
- Papers that describe EC systems using distributed computing (cloud, Mapreduce / Hadoop, grid, GPGPU, etc.) for real-world applications.
- Papers that present rigorous comparisons across techniques in a real-world application.
- Papers that present new applications of EC to real-world problems.
All contributions should be original research papers demonstrating the relevance and applicability of EC within a real-world problem. The papers with a multidisciplinary interaction are welcomed, and it is desirable that those papers are presented and written in a way that other researchers can grasp the main results, techniques, and their potential applications.
In summary, the real-world applications track is open to all domains including all industries (e.g. automobile, bio-tech, chemistry, defense, finance, oil and gas, telecommunications, etc.) and functional areas including all functions of relevance to real-world problems (e.g. logistics, scheduling, business, management, timetabling, design, data mining, process control, predictive modeling, etc.) as well as more technical and scientific disciplines (e.g. pattern recognition, computer vision, robotics, image processing, control, electrical and electronics, mechanics, etc.).
Important Dates: Submission deadline: January 13, 2012
Notification of paper acceptance: Mid March, 2012
Camera-ready submission: April 9, 2012 Conference: July 7-11, 2012
He is a computer scientist at the General Electric Global Research Center in Niskayuna, New York. As a member of the Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a member of several program committees, a member of the editorial board of the Journal of Artificial Evolution and Applications, and a Technical Editor-in-Chief of the new journal Memetic Computing. In 2006, he received the IEEE Intelligent System's "AI's 10 to Watch" award.
He is a research director of the evolutionary computer vision laboratory, EvoVisión, at the CICESE Research Center in Baja California, Mexico. Dr. Olague holds a PhD in Computer Graphics, Vision and Robotics from INPG/INRIA. He is recipient of the "2003 First Honorable Mention for the Talbert Abrams Award", offered by the American Society for Photogrammetry and Remote Sensing for his work on camera placement. Dr. Olague has won several best paper awards at major international conferences such as: GECCO and EvoStar within the Real-world application track, EvoIASP, and EvoHOT. Together with his students he has won two times the bronze medal at the Human Competitive awards for his work on the synthesis of interest point detectors and descriptors. He has been co-editor at the journals of Pattern Recognition Letters and Evolutionary Computation.
Real-world applications, distributed computing, scalability, industrial applications, scalable implementations.
Search-Based Software Engineering (SBSE) is the application of search algorithms to the solution of software engineering tasks. The SBSE track will celebrate its 11th year at GECCO in 2012. We invite papers that address various problems in the software engineering domain through the use of heuristic search techniques. We particularly encourage papers demonstrating novel search strategies or the application of SBSE techniques to new problems in software engineering. While empirical results are important, papers that do not contain results - but instead present new approaches, concepts, or theory - are also very welcome.
As an indication of the wide scope of the field, search techniques include, but are not limited to:
- Evolutionary Computation
- Simulated Annealing
- Tabu Search
- Ant Colony Optimization
- Particle Swarm Optimization
- Hybrid Algorithms
The software engineering tasks to which they are applied are drawn from throughout the engineering lifecycle and include, but are not limited to:
- Project Management and Organization
- Requirements Engineering
- Developing Dynamic Service-Oriented Systems
- Configuring Cloud-Based Architectures
- Enabling Self-healing/Self-optimizing Systems
- Creating Recommendation Systems to Support Software Development
- Software Security
- System and Software Integration
- Test Data Generation
- Regression Testing Optimization
- Network Design and Monitoring
- Software Maintenance, Program Repair,Refactoring and Transformation
Search-Based Software Engineering, Software Engineering, Software Testing, Requirements Analysis, Project Management, Software Maintenance, Software Architecture
Self-* Search :
Search and optimization problems are everywhere, and search algorithms are getting increasingly powerful. However, they are also getting increasingly complex, and only autonomous self-managed systems that provide high-level abstractions can turn search algorithms into widely used methodologies. Such systems should be able to configure themselves on the fly, automatically adapting to the changing problem conditions, based on general goals provided by their users. Self-* search systems generally incorporate ideas from adaptation, online and offline machine learning. The overall goal is to reduce the role of the human expert in the process of designing an algorithm to solve a computational search problem.
The aim of the Self-* Search track is to bring together researchers from computer science, artificial intelligence and operations research, interested in software systems able to automatically tune, configure, or even generate and design optimization algorithms and search heuristics.
We also encourage submissions related to the automated design and configuration of algorithms in other areas such as machine learning and constraint programming.
We invite all papers related to Self-* Search, in particular (but not limited to) those in the following subject areas:
2. Adaptive and self-adaptive parameter control
3. Autonomous control for search algorithms
4. Adaptive operator selection
5. Automated construction of heuristics and/or algorithms
6. Evolving heuristics and/or algorithms
7. Auto-constructive evolution
8. Cross-domain heuristic search
9. Computer-aided algorithm design
10. Multi-level search
11. Automatic algorithm configuration
12. Adaptive and co-evolutionary multimeme algorithms
13. Reactive search and intelligent optimization
14. Algorithm selection and portfolios
15. Software self-assembly
16. Meta-learning and meta-genetic programming
17. Applications of self-* techniques to multi-objective, dynamic, and
complex real-world problems.
Dr Gabriela Ochoa is a Senior Research Fellow in the Automated Scheduling, Optimisation and Planning (ASAP) research group, School of Computer Science, University of Nottingham, UK. She holds BEng and MRes degrees in Computer Science from the University Simon Bolivar, Venezuela; and a PhD in Artificial intelligence from the University of Sussex, UK. Her research interests lie in the foundations and application of evolutionary algorithms, heuristic search methods, and machine learning with emphasis in automated heuristic design, hyper-heuristics, and fitness landscape analysis. Among her research contributions are the study of error thresholds and the roles of crossover and mate selection in evolutionary algorithms; the study of heuristic search spaces, and the incorporation of complex networks in the study of combinatorial landscapes. She was involved in founding the Self-* Search (SS) track at GECCO, and proposed and co-organised the first Cross-domain Heuristic Search Challenge (CHeSC 2011) a research competition that received significant national and international attention.
Gisele L. Pappa
received her PhD in Computer Science from the University of Kent, Canterbury, UK, in 2007. She is currently
an Associate Professor at the Federal University of Minas Gerais,
Brazil. She is the author of a research-oriented book on data
mining and evolutionary algorithms, and her current research
interests are on data mining, bio-inspired computational
intelligence algorithms and social networks.
Genetic Algorithm :
The Genetic Algorithm (GA) track has always been the largest scientific track at GECCO. We invite submissions to the GA track that present original work on all aspects of genetic algorithms, including, but not limited to:
1. Practical and theoretical aspects of GAs
2. Design of new GA operators including representations, fitness functions, initialization, termination, selection, recombination, and mutation
3. Design of new and improved GAs
4. Comparisons with other methods (e.g., empirical performance analysis)
5. Hybrid approaches (e.g., memetic algorithms)
6. Design of tailored GAs for new application areas
7. Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
8. Metamodeling and surrogate assisted evolution
9. Interactive GAs
10. Co-evolutionary algorithms
11. Parameter tuning & control (including adaptation and meta-GAs)
12. Constraint Handling
13. Diversity Control (e.g., Fitness Sharing & Crowding, Automatic Speciation, Spatial models such as Island/Diffusion)
As a large and diverse track, the GA track will be an excellent opportunity to present and discuss your research/application with a wide variety of experts and participants of GECCO.
is an Associate Professor at the Department of Electronics and Computer Science at Universidade do Algarve, Portugal. He received his Ph.D. in 2000 from Universidade Nova de Lisboa, Portugal, and a Masters degree in 1997 from the University of Illinois at Urbana-Champaign, USA, having spent most of his doctoral studies at the Illinois Genetic Algorithms Laboratory (IlliGAL). He has made important contributions to Estimation of Distribution Algorithms (EDAs) and to automated parameter selection techniques for genetic and evolutionary algorithms, having co-edited a book in that topic, "Parameter Setting in Evolutionary Algorithms".
He is an Associate Professor in the Department of Computer Science at Missouri University of Science and Technology (S&T), the founding director of S&T's Natural Computation Laboratory (NC-LAB), his department's Undergraduate Committee Chair, and S&T's Campus Curricula Committee Chair. He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. He served previously as the GECCO 2010 Late Breaking Papers Chair and the COMPSAC 2011 Doctoral Symposium Chair. For several years he has served on the GECCO GA track program committee and the Congress on Evolutionary Computation program committee. His research interests include the design of novel self-configuring evolutionary algorithms and the application of evolutionary algorithms in critical infrastructure protection, search-based software engineering, virtual facilitation, and cyber security. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.
genetic algorithms, evolutionary algorithms, selection, recombination, mutation, representation, fitness function, genetic operators, parameters, adaptation, uncertainty, co-evolution, constraint handling, diversity control, metamodeling
Integrative Genetic and Evolutionary Computation (IGEC):
GECCO has traditionally been a collection of mini-conferences, so authors have to choose a particular track to submit to. While this works fine for the majority of papers, some authors struggled to choose a track. This is why GECCO now has a new track on "Integrative Genetic and Evolutionary Computation (IGEC)".
This track welcomes all papers that the authors feel do not fit into a particular track or that cross multiple tracks.
Topics include but are not limited to:
- Research on combining multiple evolutionary algorithms, such as Genetic Algorithms, Evolutionary Programming, Evolution Strategies and others
- Classification of evolutionary algorithms
- Hybrid algorithms in general
- Novel nature-inspired paradigms
- Dynamic and stochastic environments
- Statistical analysis techniques for EAs
- Evolutionary algorithm toolboxes
- Evolutionary algorithms with expensive fitness evaluations
- Ensemble methods
He holds Deva Raj Chair Professor at
Indian Institute of Technology Kanpur in India. He is the recipient of
the prestigious MCDM Edgeworth-Pareto award by the Multiple Criterion
Decision Making (MCDM) Society, one of the highest awards given in the
field of multi-criterion optimization and decision making.
He has also received prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences
for the year 2005 from Govt. of India.
He has also received the `Thomson Citation Laureate
Award' from Thompson Scientific for having highest number of citations
in Computer Science during the past ten years in India.
He is a fellow of Indian National Academy of
Engineering (INAE), Indian National Academy of Sciences,
and International Society of Genetic and Evolutionary
Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research
award from Alexander von Humboldt Foundation in 2003. His main research
interests are in the area of computational optimization, modeling and
design, and evolutionary algorithms. He has written two text books
on optimization and more than 240 international journal and conference
research papers. He has pioneered and a leader in the field of
evolutionary multi-objective optimization. He is associate editor of two
major international journals and an editorial board members of five
More information about his research can be found from
SIGEVO Executive Committee
Erik D. Goodman
|Pier Luca Lanzi
Darrell Whitley (chair)