The Class Algorithm: Evolution Based on Division of Labor and Specialization

Yangyang Chang, Gerald E. Sobelman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes the class algorithm, a new type of evolutionary algorithm. The methodology is inspired by the concepts of division of labor and specialization. Individuals form subpopulations of different classes, where each class has its own characteristics. The entire population evolves through influences among individuals within and between the different subpopulations. The proposed approach can be applied in both continuous and discrete problem domains. The performance of the class algorithm surpasses other evolutionary algorithms for many test functions of single-objective continuous optimization benchmark problems. The class algorithm also shows a competent ability to solve the large-scale discrete optimization problems.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-215
Number of pages7
ISBN (Electronic)9798350396454
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 - Virtual, Online, Indonesia
Duration: Nov 24 2022Nov 26 2022

Publication series

NameProceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

Conference

Conference2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period11/24/2211/26/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • continuous optimization
  • discrete optimization
  • evolutionary algorithm
  • genetic algorithm

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