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Communication Dans Un Congrès Année : 2022

General Univariate Estimation-of-Distribution Algorithms

Résumé

We propose a general formulation of a univariate estimationof-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs compact genetic algorithm, univariate marginal distribution algorithm and population-based incremental learning as well as the max-min ant system with iteration-best update. Our unified description of the existing algorithms allows a unified analysis of these; we demonstrate this by providing an analysis of genetic drift that immediately gives the existing results proven separately for the four algorithms named above. Our general model also includes EDAs that are more efficient than the existing ones and these may not be difficult to find as we demonstrate for the OneMax and LeadingOnes benchmarks.
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Dates et versions

hal-03797600 , version 1 (04-10-2022)

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Benjamin Doerr, Marc Dufay. General Univariate Estimation-of-Distribution Algorithms. Parallel Problem Solving from Nature (PPSN 2022), Sep 2022, Dortmund, Germany. ⟨10.1007/978-3-031-14721-0_33⟩. ⟨hal-03797600⟩
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