Selecting survivors in genetic algorithm using tabu search strategies
| Autor: | Chih-Hui Huang, Chuan-Kang Ting, Cheng-Feng Ko |
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| Rok vydání: | 2009 |
| Předmět: |
Mathematical optimization
Control and Optimization Diversity maintenance General Computer Science business.industry Computer science Combinatorial optimization problem Machine learning computer.software_genre Tabu search Genetic algorithm Convergence (routing) Key (cryptography) Artificial intelligence business computer Selection operator Selection (genetic algorithm) |
| Zdroj: | Memetic Computing. 1:191-203 |
| ISSN: | 1865-9292 1865-9284 |
| Popis: | Genetic algorithm (GA) is well-known for its effectiveness in global search and optimization. To balance selection pressure and population diversity is an important issue of designing GA. This paper proposes a novel hybridization of GA and tabu search (TS) to address this issue. The proposed method embeds the key elements of TS—tabu restriction and aspiration criterion—into the survival selection operator of GA. More specifically, the tabu restriction is used to prevent inbreeding for diversity maintenance, and the aspiration criterion is activated to provide moderate selection pressure under the tabu restriction. The interaction of tabu restriction and aspiration criterion enables survivor selection to balance selection pressure and population diversity. The experimental results on numerical and combinatorial optimization problems show that this hybridization can significantly improve GAs in terms of solution quality as well as convergence speed. An empirical analysis further identifies the influences of the TS strategies on the performance of this hybrid GA. |
| Databáze: | OpenAIRE |
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