Effect of a push operator in genetic algorithms for multimodal optimization

Genetic Algorithms have been successfully used to solve multi-modal optimization problems, mainly due to their population approach and implicit parallel processing among multiple subpopulations. In order to find and maintain multiple regions, GAs implement a niching principle motivated from nature. The selection procedure of a GA is modified by restricting a comparison among similar solutions to bring about an additional level of diversity in the population. In another recent study, a real-parameter push-operator based on non-uniform coding principle applied to binary-coded GAs was proposed. The push-operator has shown to exhibit better convergence properties on many optimization problems compared to standard GA implementations. In this paper, we extend the push-operator and its implementation with the niching principle to solve multi-modal problems. On a number of constrained and unconstrained multi-modal test problems, we demonstrate its superior convergence to multiple optimal solutions simultaneously. Results are interesting and motivate us to extend the push-operator to multi-objective and other complex optimization tasks.

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