self-adaptive mutation rates in genetic algorithm for inverse design of cellular automata
Self-adaptation is used a lot in Evolutionary Strategies and with great success, yet for some reason it is not the mutation adaptation of choice for Genetic Algorithms. This poster describes how a self-adaptive mutation rate was used in a Genetic Algorithms to inverse design behavioral rules for a Cellular Automata. The unique characteristics of this search space gave rise to some interesting convergence behavior that might have implications for using self-adaptive mutation rates in other Genetic Algorithm applications and might clarify why self-adaptation in Genetic Algorithms is less successful than in Evolutionary Strategies.
genetic algorithms cellular automata self adaptation