Improvements of Real-coded Genetic Algorithms for Solving Multi-modal Problems

During last years, a lot of optimization strategies were developed including genetic algorithms, especially because of their robustness and their very limited requirements on a solved problem. Nevertheless, an optimization of multi-modal problems remains computationally very expensive process. In this paper, several improvements are proposed to the SADE genetic algorithm in order to increase the speed of convergence and reduce the number of tuning parameters. A previously proposed niching strategy is combined with the new version of a genetic algorithm to improve performance on multi-modal problems using several re-starts of an optimization process and memorizing found local extremes.

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