Soft Genetic Operators in Evolutionary Algorithms
نویسنده
چکیده
With this paper soft genetic operators for Evolutionary Algorithms are introduced and analyzed for multimodal continuous parameter optimization problems. A new scaling rule for multiple mutations is formalized and compared with a new step-size scaling for Evolution Strategies. The scaling of the Evolutionary Algorithm with Soft genetic operators (EASY) is compared with that of the Breeder Genetic Algorithm (BGA). A performance comparison of EASY with recently published results concerning the performance of Bayesian/Sampling and Very Fast Simulated Reannealing techniques for global optimization is given.
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تاریخ انتشار 1995