Sexual Selection for Genetic Algorithms
Publication Type
Journal Article
Publication Date
4-2003
Abstract
Genetic Algorithms (GA) have been widely used in operations research and optimization since first proposed. A typical GA comprises three stages, the encoding, the selection and the recombination stages. In this work, we focus our attention on the selection stage of GA, and review a few commonly employed selection schemes and their associated scaling functions.We also examine common problems and solution methods for such selection schemes. We then propose a new selection scheme inspired by sexual selection principles through female choice selection, and compare the performance of this new scheme with commonly used selection methods in solving some well-known problems including the Royal Road Problem, the Open Shop Scheduling Problem and the Job Shop Scheduling Problem.
Keywords
Genetic algorithm, Scheduling, Selection
Discipline
Operations and Supply Chain Management
Research Areas
Operations Management
Publication
Artificial Intelligence Review
Volume
19
Issue
2
First Page
123
Last Page
152
ISSN
0269-2821
Identifier
10.1023/A:1022692631328
Publisher
Springer Verlag
Citation
GOH, Kai Song; LIM, Andrew; and Rodrigues, Brian.
Sexual Selection for Genetic Algorithms. (2003). Artificial Intelligence Review. 19, (2), 123-152.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/2147
Additional URL
https://doi.org/10.1023/A:1022692631328