Literature DB >> 16297278

On the choice of the offspring population size in evolutionary algorithms.

Thomas Jansen1, Kenneth A De Jong, Ingo Wegener.   

Abstract

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

Mesh:

Year:  2005        PMID: 16297278     DOI: 10.1162/106365605774666921

Source DB:  PubMed          Journal:  Evol Comput        ISSN: 1063-6560            Impact factor:   3.277


  3 in total

1.  On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation.

Authors:  Dogan Corus; Jun He; Thomas Jansen; Pietro S Oliveto; Dirk Sudholt; Christine Zarges
Journal:  Algorithmica       Date:  2016-08-18       Impact factor: 0.791

2.  How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism.

Authors:  Pietro S Oliveto; Tiago Paixão; Jorge Pérez Heredia; Dirk Sudholt; Barbora Trubenová
Journal:  Algorithmica       Date:  2017-09-06       Impact factor: 0.791

3.  A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization.

Authors:  Andrei Lissovoi; Carsten Witt
Journal:  Algorithmica       Date:  2016-12-07       Impact factor: 0.791

  3 in total

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