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
Dirk Thierens
Utrecht University, The Netherlands
Dr. Dirk Thierens is a lecturer/senior researcher 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.Carlos Coello Coello
CINVESTAV-IPN, 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, which is an area in which he has made pioneering contributions. He currently has over 500 publications which, according to Google Scholar, report over 58,800 citations (with an h-index of 96). 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, the Ciudad Capital: Heberto Castillo 2011 Award for scientists under the age of 45, in Basic Science, the 2012 Scopus Award (Mexico's edition) for being the most highly cited scientist in engineering in the 5 years previous to the award 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). He also received the Luis Elizondo Award from the Instituto Tecnológico de Monterrey in 2019. He is the recipient of the prestigious 2013 IEEE Kiyo Tomiyasu Award, ""for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics"", and of the 2016 The World Academy of Sciences (TWAS) Award in ?Engineering Sciences?. Since January 2011, he is an IEEE Fellow. He is also the recipient of the 2021 IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award. He is also Associate Editor of several international journals including Evolutionary Computation and the IEEE Transactions on Emerging Topics in Computational Intellience. Since January 2021, he is the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation. He is currently Full Professor with distinction at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.