Constraint-Handling Techniques used with Evolutionary Algorithms
Description
Evolutionary Algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty
factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in
the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches
such as the use of tournament rules based on feasibility, repair methods, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative
search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be also discussed.
Organizers
CINVESTAV, Mexico
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.