Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Learning environment dynamics from self-adaptation: a preliminary investigation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Challenges and opportunities in dynamic optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Evolutionary algorithms applied to dynamic optimization problems has become a promising research area. So far, all papers in the area have assumed that the environment changes only between generations. In this paper, we take a first look at possibilities to handle a change during a generation. For that purpose, we derive an analytical model for a (1, 2) evolution strategy and show that sometimes it is better to ignore the environmental change until the end of the generation, than to evaluate each individual with the most up-to-date fitness function.