Chaotic genetic algorithm and the effects of entropy in performance optimization

Guillermo Fuertes, Manuel Vargas, Miguel Alfaro, Rodrigo Soto-Garrido, Jorge Sabattin, María Alejandra Peralta

Resultado de la investigación: Contribución a una revistaArtículo

1 Cita (Scopus)

Resumen

This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory, the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm. The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm.

Idioma originalInglés
Número de artículo013132
PublicaciónChaos
Volumen29
N.º1
DOI
EstadoPublicada - 1 ene 2019

Áreas temáticas de ASJC Scopus

  • Física estadística y no lineal
  • Física matemática
  • Física y astronomía (todo)
  • Matemáticas aplicadas

Huella Profundice en los temas de investigación de 'Chaotic genetic algorithm and the effects of entropy in performance optimization'. En conjunto forman una huella única.

  • Citar esto

    Fuertes, G., Vargas, M., Alfaro, M., Soto-Garrido, R., Sabattin, J., & Peralta, M. A. (2019). Chaotic genetic algorithm and the effects of entropy in performance optimization. Chaos, 29(1), [013132]. https://doi.org/10.1063/1.5048299