Advancing continuous IDEAs with mixture distributions and factorization selection metrics

Evolutionary optimization based on probabilistic models has so far been limited to the use of factorizations in the case of continuous representations. Furthermore, a maximum complexity parameter K was required previously to construct factorizations to prevent unnecessary complexity to be introduced in the factorization. In this paper, we advance these techniques by using clustering and the EM algorithm to allow for mixture distributions. Furthermore, we apply a search metric to eliminate the K parameter. We use these techniques in the IDEA framework to obtain new continuous evolutionary optimization algorithms and investigate their performance.

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