A Novel Essential Mutation Method for Evolutionary Algorithms

Iyad Abu Doush, Mohammed A. Awadallah, Mohammed Azmi Al-Betar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The mutation is one of the operators that is used by many Evolutionary Algorithms (EA) to diversify the population (solutions). It can enhance the algorithm exploration of the problem search space and improve the evolution process. This paper introduces a novel mutation technique that is based on a recently investigated mutation bias pattern in the Arabidopsis thaliana plant [1]. The proposed mutation technique is called an essential mutation. The proposed method uses the ϵ parameter to control the amount of distance we can be from the parent's fitness. Three different configurations are studied and the best results are obtained when ϵ=0. It is compared against five well-known mutation techniques which are Boundary, Non-uniform, MPT, and Polynomial on standard benchmark functions. The obtained results show the superiority of the proposed essential mutation in terms of best solution and convergence speed in most of the test functions.

Original languageEnglish
Title of host publication2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings
EditorsFausto Pedro Garcia Marquez, Akhtar Jamil, Alaa Ali Hameed
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474832
DOIs
StatePublished - 2022
Event2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Istanbul, Turkey
Duration: 15 Jul 202216 Jul 2022

Publication series

Name2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings

Conference

Conference2nd International Conference on Computing and Machine Intelligence, ICMI 2022
Country/TerritoryTurkey
CityIstanbul
Period15/07/2216/07/22

Keywords

  • Biased mutation
  • Evolutionary Algorithms
  • Meta-heuristic algorithm
  • Mutation
  • Polynomial mutation

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