Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization
作者： Pedro J. Ballester , Jonathan N. Carter
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摘要: The aim of this paper is to identify Genetic Algorithms (GAs) which perform well over a range continuous and smooth multimodal real-variable functions. In our study, we focus on testing GAs combining three classes genetic operators: selection, crossover replacement. approach followed time-constrained thus stopping criterion fixed number generations. Results show that with random selection parents crowding replacement are robust optimizers. By contrast, tournament poorly in comparison.
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