A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. In this paper we look at the efficiency of mixing in genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). We compute the mixing probabilities of two partial solutions and discuss the effect of the covariance build-up in GAs and EDas. Moreover, we propose two new Evolutionary Algorithms that maximize the juxtaposing of the partial solutions present in the parents: the Recombinative Optimal Mixing Evolutionary Algorithm (ROMEA) and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA).
Additional Metadata
Keywords Genetic Algorithms, Estimation-of-Distribution Algorithms, Optimal Mixing, Linkage Tree Genetic Algorithm
ACM Problem Solving, Control Methods, and Search (acm I.2.8)
THEME Software (theme 1), Logistics (theme 3), Energy (theme 4)
Publisher ACM
Editor N. Krasnogor
Conference Genetic and Evolutionary Computation Conference
Citation
Thierens, D, & Bosman, P.A.N. (2011). Optimal Mixing Evolutionary Algorithms. In N Krasnogor (Ed.), Proceedings of ACM Annual Genetic and Evolutionary Computation Conference 2011 (pp. 617–624). ACM.