Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Religion-Based Spatial Model for Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Strategy Adaption by Competing Subpopulations
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Applying Self-Organised Criticality to Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
AORCEA - An adaptive operator rate controlled evolutionary algorithm
Computers and Structures
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The optimal choice of the variation operators mutation and crossover and their parameters can be decisive for the performance of evolutionary algorithms (EAs). Usually the type of the operators (such as Gaussian mutation) remains the same during the entire run and the probabilistic frequency of their application is determined by a constant parameter, such as a fixed mutation rate. However, recent studies have shown that the optimal usage of a variation operator changes during the EA run. In this study, we combined the idea of self-adaptive mutation operator scheduling with the Religion-Based EA (RBEA), which is an agent model with spatially structured and variable sized subpopulations (religions). In our new model (OSRBEA), we used a selection of different operators, such that each operator type was applied within one specific subpopulation only. Our results indicate that the optimal choice of operators is problem dependent, varies during the run, and can be handled by our self-adaptive OSRBEA approach. Operator scheduling could clearly improve the performance of the already very powerful RBEA and was superior compared to a classic and other advanced EA approaches.