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GA - Genetic Algorithms

Description

The Genetic Algorithm (GA) track has always been a large and important 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:

  • Practical, methodological and foundational aspects of GAs
  • Design of new GA operators including representations, fitness functions, initialization, termination, parent selection, replacement strategies, recombination, and mutation
  • Design of new and improved GAs
  • Fitness landscape analysis
  • Comparisons with other methods (e.g., empirical performance analysis)
  • Design of tailored GAs for new application areas
  • Handling uncertainty (e.g., dynamic and stochastic problems, robustness)
  • Metamodeling and surrogate assisted evolution
  • Interactive GAs
  • Co-evolutionary algorithms
  • Parameter tuning and control (including adaptation and meta-GAs)
  • Constraint Handling
  • Diversity management (e.g., fitness sharing and crowding, automatic speciation, spatial models such as island/diffusion)
  • Bilevel and multi-level optimization
  • Ensemble based genetic algorithms
  • Model-Based Genetic Algorithms


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.

Track Chairs

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Dirk Thierens

Utrecht University, Netherlands

Dr. Dirk Thierens is an associate professor at the Department of Information and Computing Sciences at Utrecht University, where he is teaching courses on Evolutionary Computation and Computational Intelligence. He has (co)-authored over 100 peer reviewed papers in Evolutionary Computation. His main current research interests are focused on the design and application of structure learning techniques in the framework of population-based, stochastic search. Dirk contributed to the organization of previous GECCO conferences as track chair, workshop organizer, Editor-in-Chief, and past member of the SIGEVO ACM board.

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Carlos Coello Coello

CINVESTAV, Mexico | webpage

Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. His research has mainly focused on the design of new multi-objective optimization algorithms based on bio-inspired metaheuristics (e.g., evolutionary algorithms), which is an area in which he has made pioneering contributions. He currently has more than 600 publications, including more than 200 journal papers and 50 book chapters. His publications currently report over 79,920 citations in Google Scholar (his h-index is 107). He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science (in the area of exact sciences), the 2009 Medal to the Scientific Merit from Mexico City's congress and the 2012 National Medal of Science in Physics, Mathematics and Natural Sciences from Mexico's presidency (this is the most important award that a scientist can receive in Mexico). Additionally, he is the recipient of the 2013 IEEE Kiyo Tomiyasu Award, "for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics", of the 2016 The World Academy of Sciences (TWAS) Award in “Engineering Sciences”, and of the 2021 IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award. Since January 2011, he is an IEEE Fellow. He is currently the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation. He is Full Professor with distinction (Investigador Cinvestav 3F) at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.