Abstract
The use of balanced crossover operators in Genetic Algorithms (GA) ensures that the binary strings generated as offsprings have the same Hamming weight of the parents, a constraint which is sought in certain discrete optimization problems. Although this method reduces the size of the search space, the resulting fitness landscape often becomes more difficult for the GA to explore and to discover optimal solutions. This issue has been studied in this paper by applying an adaptive bias strategy to a counter-based crossover operator that introduces unbalancedness in the offspring with a certain probability, which is decreased throughout the evolutionary process. Experiments show that improving the exploration of the search space with this adaptive bias strategy is beneficial for the GA performances in terms of the number of optimal solutions found, even if these benefits are not reflected in the resulting fitness distributions.
Original language | English |
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Title of host publication | 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW) |
Subtitle of host publication | Proceedings |
Editors | R. Bilof |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 234-240 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-6654-2835-4 |
ISBN (Print) | 978-1-6654-1218-6 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW) - Matsue, Japan Duration: 23 Nov 2021 → 26 Nov 2021 Conference number: 9th |
Conference
Conference | 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW) |
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Country/Territory | Japan |
City | Matsue |
Period | 23/11/21 → 26/11/21 |
Keywords
- Genetic algorithms
- crossover operators
- boolean functions
- balancedness
- nonlinearity