Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
An Analysis of Dynamic Severity and Population Size
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Frequency and severity are a priori very influential parameters in the performance of Dynamic Optimization Problems because they establish when and how hard is the change of the target optimized function. We study in a systematic way their influence in the performance of Dynamic Optimization Problems and the possible mathematical correlations between them. Specifically, we have used a steady state Genetic Algorithm, which has been applied to three classic Dynamic Optimization Problems considering a wide range of frequency and severity values. The results show that the severity is the more important parameter influencing the accuracy of the algorithm.