A new strategy for adapting the mutation probability in genetic algorithms
Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimizati...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2012
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23593 |
| Aporte de: |
| Sumario: | Traditionally in Genetic Algorithms, the mutation probability parameter maintains a constant value during the search. However, an important difficulty is to determine a priori which probability value is the best suited for a given problem. Besides, there is a growing demand for up-to-date optimization software, applicable by a non-specialist within an industrial development environment. These issues encourage us to propose an adaptive evolutionary algorithm that includes a mechanism to modify the mutation probability without external control. This process of dynamic adaptation happens while the algorithm is searching for the problem solution. This eliminates a very expensive computational phase related to the pre-tuning of the algorithmic parameters. We compare the performance of our adaptive proposal against traditional genetic algorithms with fixed parameter values in a numerical way. The empirical comparisons, over a range of NK-Landscapes instances, show that a genetic algorithm incorporating a strategy for adapting the mutation probability outperforms the same algorithm using fixed mutation rates. |
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