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 language | English (US) |
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Title of host publication | Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 209-215 |
Number of pages | 7 |
ISBN (Electronic) | 9798350396454 |
DOIs | |
State | Published - 2022 |
Event | 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 - Virtual, Online, Indonesia Duration: Nov 24 2022 → Nov 26 2022 |
Publication series
Name | Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 |
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Conference
Conference | 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 |
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Country/Territory | Indonesia |
City | Virtual, Online |
Period | 11/24/22 → 11/26/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- continuous optimization
- discrete optimization
- evolutionary algorithm
- genetic algorithm