A new strategy for adapting the mutation probability in genetic algorithms

• Autores: Stark, Natalia; Minetti, Gabriela F.; Salto, Carolina
• Año de publicación: 2012
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Adapting the Mutation Probability; Genetic Algorithms; Algorithms; Intelligent agents
• Institución: Universidad Nacional de La Plata

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.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)