Chaotic Mapping Genetic Algorithm with Multiple Strategies

Qianyu Zhu, Yifei Yang, Haotian Li, Haichuan Yang, Baohang Zhang, Shangce Gao*

*Corresponding author for this work

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

1 Scopus citations

Abstract

The genetic algorithm is a very successful metaheuristic algorithm. Its wide applications in recommendation algorithms, feature selection, and industrial design have proven its effectiveness. However, it still suffers from the issues of low performance and local optima. Therefore, we propose a method called Chaotic Mapping Genetic Algorithm with Multiple Strategies (CGA-M) inspired by the existence of chaos in nature's evolution and shorten the evolutionary process by the linear decline method. We test its effectiveness on the classical test set from IEEE congress on evolutionary computation (IEEE CEC). The experimental results demonstrated its superiority.

Original languageEnglish
Title of host publication2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350321456
DOIs
StatePublished - 2023
Event15th International Conference on Advanced Computational Intelligence, ICACI 2023 - Seoul, Korea, Republic of
Duration: 2023/05/062023/05/09

Publication series

Name2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023

Conference

Conference15th International Conference on Advanced Computational Intelligence, ICACI 2023
Country/TerritoryKorea, Republic of
CitySeoul
Period2023/05/062023/05/09

Keywords

  • Chaotic Mapping
  • Elite Bootstrap Strategy
  • Genetic Algorithm
  • Linear Decline

ASJC Scopus subject areas

  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Artificial Intelligence

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