A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization - Archive ouverte HAL
Article Dans Une Revue ACM Transactions on Evolutionary Learning and Optimization Année : 2021

A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

Résumé

The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions, and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.
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Dates et versions

hal-03408953 , version 1 (29-10-2021)

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Benjamin Doerr, Frank Neumann. A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. ACM Transactions on Evolutionary Learning and Optimization, 2021, 1 (4), pp.1-43. ⟨10.1145/3472304⟩. ⟨hal-03408953⟩
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