Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
Influence of the migration period in parallel distributed GAs for dynamic optimization
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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In a stationary optimization problem, the fitness landscape does not change during the optimization process; and the goal of an optimization algorithm is to locate a stationary optimum. On the other hand, most of the real world problems are dynamic, and stochastically change over time. Genetic Algorithms have been applied to dynamic problems, recently. In this study, we present two hybrid techniques that are applied on moving peaks benchmark problem, where these techniques are the extensions of the leading methods in the literature. Based on the experimental study, it was observed that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem.