Abstract
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. Empirical experiments carried out this paper confirm us the effectiveness of the proposed methods.
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Handa, H. (2005). Estimation of Distribution Algorithms with Mutation. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_11
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DOI: https://doi.org/10.1007/978-3-540-31996-2_11
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