Journal Article
CSGA: A dual population Genetic Algorithm based on Mexican Cavefish genetic diversity
Alkafaween, E., Hassanat, A., Essa, E. and Elmougy, S.
Record Number:
6451
Year:
2024
Journal:
Jordanian Journal of Computers and Information Technology
Pages:
231-246
Volume:
10
Notes:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Abstract:
Genetic algorithms (GAs) are search algorithms based on population genetics and natural-selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population GA and inspired by the genetic variety observed in Mexican cavefish. Through inter-population cross-breeding, CSGA increases diversity via a secondary population (cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity-preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP.
KEYWORDS
Cave-surface GA, Diversity, GAs, Premature convergence.
Times Cited:
1
Relevent Species: