Fast Mutation in Crossover-based Algorithms - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2021

Fast Mutation in Crossover-based Algorithms

Résumé

The heavy-tailed mutation operator proposed in Doerr, Le, Makhmara, and Nguyen (GECCO 2017), called \emph{fast mutation} to agree with the previously used language, so far was proven to be advantageous only in mutation-based algorithms. There, it can relieve the algorithm designer from finding the optimal mutation rate and nevertheless obtain a performance close to the one that the optimal mutation rate gives. In this first runtime analysis of a crossover-based algorithm using a heavy-tailed choice of the mutation rate, we show an even stronger impact. For the $(1+(\lambda,\lambda))$ genetic algorithm optimizing the OneMax benchmark function, we show that with a heavy-tailed mutation rate a linear runtime can be achieved. This is asymptotically faster than what can be obtained with any static mutation rate, and is asymptotically equivalent to the runtime of the self-adjusting version of the parameters choice of the $(1+(\lambda,\lambda))$ genetic algorithm. This result is complemented by an empirical study which shows the effectiveness of the fast mutation also on random satisfiable Max-3SAT instances.
Fichier principal
Vignette du fichier
2004.06538.pdf (387.89 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03311184 , version 1 (30-07-2021)

Identifiants

  • HAL Id : hal-03311184 , version 1

Citer

Denis Antipov, Maxim Buzdalov, Benjamin Doerr. Fast Mutation in Crossover-based Algorithms. 2021. ⟨hal-03311184⟩
28 Consultations
78 Téléchargements

Partager

More