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

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number013132
Issue number1
Publication statusPublished - 1 Jan 2019

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics


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