Mutation Can Improve the Search Capability of Estimation of Distribution Algorithms
نویسنده
چکیده
The Estimation of Distribution Algorithms are a class of evolutionary algorithms which adopt probabilistic models to reproduce the genetic information of the next generation, instead of conventional crossover and mutation operations. In this paper, we propose new EDAs which incorporate mutation operator to conventional EDAs in order to keep the diversities in EDA populations. Experiments results shown in this paper confirm us the effectiveness of the proposed methods.
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