Fitness-dependent hybridization of clonal selection algorithm and random local search

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Artificial immune systems (AIS) and local search algorithms have remarkable differences in the structure of mutation operators. Thus AIS algorithms may be more efficient at the beginning of optimization, while local search algorithms are more efficient in the end, when we need to do small improvements. Our goal is to combine several mutation operators in one algorithm so that the new algorithm will be efficient on fixed budget and will reach optimum within reasonable time bounds. We propose to select mutation operators used in AIS and local search according to a specific exponential probability function which depends on the fitness of the current individual. During the experimental study, we constructed hybrids from AIS mutation operator CLONALG (Clonal Selection Algorithm) and RLS mutation operator (Random Local Search) and used them to solve OneMax problem. We compared the proposed method with a simple hybrid algorithm and empirically confirmed the hypothesis that hybrids are efficient on fixed budget and need only a slightly higher number of iterations to reach the optimum.

Original languageEnglish
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherAssociation for Computing Machinery, Inc
Pages5-6
Number of pages2
ISBN (Electronic)9781450343237
DOIs
Publication statusPublished - 20 Jul 2016
Externally publishedYes
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States of America
Duration: 20 Jul 201624 Jul 2016

Publication series

NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Conference

Conference2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
Country/TerritoryUnited States of America
CityDenver
Period20 Jul 201624 Jul 2016

Keywords

  • AIS
  • Artificial immune systems
  • Hybrid algorithms
  • RLS

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