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Program Tracks


Three days of presentations of the latest high-quality results in 13 separate and independent program tracks specializing in various aspects of genetic and evolutionary computation.

TitleOrganizers
ACO-SI - Ant Colony Optimization and Swarm Intelligence
  • Xiaodong Li
  • Martin Middendorf
CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)
  • Risto Miikkulainen
  • Emma Hart
DETA - Digital Entertainment Technologies and Arts
  • Ekart Aniko
  • Daniele Loiacono
ECOM - Evolutionary Combinatorial Optimization and Metaheuristics
  • Holger Hoos
  • Sébastien Verel
EML - Evolutionary Machine Learning
  • Mengjie Zhang
  • Will Browne
EMO - Evolutionary Multiobjective Optimization
  • Tea Tušar
  • Carlos M. Fonseca
ENUM - Evolutionary Numerical Optimization
  • Nikolaus Hansen
  • Marcus Gallagher
GA - Genetic Algorithms
  • Dirk Thierens
  • Alberto Moraglio
GECH - General Evolutionary Computation and Hybrids
  • Jim Smith
  • Jürgen Branke
GP - Genetic Programming
  • Sara Silva
  • Zdenek Vasicek
RWA - Real World Applications
  • Anna I Esparcia-Alcázar
  • Boris Naujoks
SBSE - Search-Based Software Engineering
  • Simon Poulding
  • Federica Sarro
THEORY - Theory
  • Carola Doerr
  • Dirk Sudholt

  

ACO-SI - Ant Colony Optimization and Swarm Intelligence

Description

Swarm Intelligence (SI) is the collective problem-solving behavior of groups of animals or artificial agents that results from the local interactions of the individuals with each other and with their environment. SI systems rely on certain key principles such as decentralization, stigmergy, and self-organization. Since these principles are observed in the organization of social insect colonies and other animal aggregates, such as bird flocks or fish schools, SI systems are typically inspired by these natural systems.
The two main application areas of SI have been optimization and robotics. In the first category, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) constitute two of the most popular SI optimization techniques with numerous applications in science and engineering. In the second category, SI has been successfully used to control large numbers of robots in a decentralized way, which increases the flexibility, robustness, and fault-tolerance of the resulting systems.

Scope

The ACO-SI Track welcomes submissions of original and unpublished work in all experimental and theoretical aspects of SI, including (but not limited to) the following areas:

  • Biological foundations
  • Modeling and analysis of new approaches
  • Hybrid schemes with other algorithms
  • Multi-swarm and self-adaptive approaches
  • Constraint-handling and penalty function approaches
  • Combinations with local search techniques
  • Benchmarking and new empirical results
  • Parallel/distributed implementations and applications
  • Large-scale applications
  • Applications to multi-objective, dynamic, and noisy problems
  • Applications to continuous and discrete search spaces
  • Software and high-performance implementations
  • Theoretical and experimental research in swarm robotics

Biographies

Xiaodong Li

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".

Martin Middendorf

He is Professor of Parallel Computing and Complex Systems at the University of Leipzig, Germany. He received the Diploma degree in mathematics and the Dr. rer. nat. degree from the University of Hannover, Germany and the Professoral Habilitation degree from the University of Karlsruhe, Germany. He has worked at the University of Dortmund, Germany and the University of Hannover as a Visiting Professor of Computer Science. He was a Professor of Computer Science at the Catholic University of Eichstätt, Germany. His research interests include nature inspired algorithms, swarm intelligence, bioinformatics, and self-organised systems.

     

CS - Complex Systems (Artificial Life/Artificial Immune Systems/Generative and Developmental Systems/Evolutionary Robotics/Evolvable Hardware)

Description

This track invites all papers addressing the challenges of scaling evolution up to real-life complexity. This includes both the real-life complexity of biological systems, such as artificial life, artificial immune systems, and generative and developmental systems (GDS); and the real-world complexity of physical systems, such as evolutionary robotics and evolvable hardware.

Artificial life, Artificial Immune Systems, and Generative and Developmental Systems all take inspiration from studying living systems. In each field, there are generally two main complementary goals: to better understand living systems and to use this understanding to build artificial systems with properties similar to those of living systems, such as behavior, adaptability, learning, developmental or generative processes, evolvability, active perception, communication, self-organization and cognition. The track welcomes both theoretical and application-oriented studies in the above fields. The track also welcomes models of problem-solving through (social) agent interaction, emergence of collective phenomena and models of the dynamics of ecological interactions in an evolutionary context.

Evolutionary Robotics and Evolvable Hardware study the evolution of controllers, morphologies, sensors, and communication protocols that can be used to build systems that provide robust, adaptive and scalable solutions to the complexities introduced by working in real-world, physical environments. The track welcomes contributions addressing problems from control to morphology, from single robot to collective adaptive systems. Approaches to incorporating human users into the evolutionary search process are also welcome. Contributions are expected to deal explicitly with Evolutionary Computation, with experiments either in simulation or with real robots.

Biographies

Risto Miikkulainen

Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a Fellow at Sentient Technologies, Inc. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His recent research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing, and self-organization of the visual cortex; he is an author of over 3;0 articlesity of Texas Gdo3g are12. He is a "IEEa Fell,ng member of thBoAwarr oGf ovnorels of thN neuraN networS(soetrgy, ans anunctioe Editor oC cognivune Systece Resear), and"IEEE Transactions oy Computaticial Intelligencs andIesitGacomd".

     

DETA - Digital Entertainment Technologies and Arts

Description

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Scope

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    • learnin, ans adaptatid, igacomp>
    • esearcg methodel oracomp>
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    • yny ofimor ofnteatusms,gImasng, imntations
    • tyeuristrred cognitinn and clartifizatip>t

Biographies

     

ECOM - Evolutionary Combinatorial Optimization and Metaheuristics

Description

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Scope

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  • Theoretica, developmeuey Iy combinatoriaI optimizatioa and metaheuristims
  • esearch spaic aninlascadeve analyses
  • Cl isicons ne beediffoherenn technique(, includinexmpacg metho)es
  • Constraint-handlinh techniques
  • Hybrig methomse adaptivh Hybrimization techniquea and opistoc Compssies
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EML - Evolutionary Machine Learning

Description

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Scope

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