Self-adjusting a Genetic Algorithm Using Fuzzy Logic Techniques

  • Mario César López-Locés Tecnológico Nacional de Mexico, Instituto Tecnológico de Ciudad Madero
  • Héctor Joaquín Fraire-Huacuja Tecnológico Nacional de Mexico, Instituto Tecnológico de Ciudad Madero
  • Rodolfo Pazos Rangel Tecnológico Nacional de Mexico, Instituto Tecnológico de Ciudad Madero
  • Juan J. González Barbosa Tecnológico Nacional de Mexico, Instituto Tecnológico de Ciudad Madero
  • Jesús David Terán Villanueva Tecnológico Nacional de Mexico, Instituto Tecnológico de Ciudad Madero
Keywords: fuzzy logic, parameter tuning, genetic algorithm

Abstract

One of the most important tasks in approximately solving an optimisation problem is to adjust the parameters of the metaheuristic used as a solution method. As the metaheuristics are usually general in purpose, it is necessary to make adjustments to them for each optimisation problem to which they are applied to get high-quality solutions. In this paper, we propose the use of a Type 1 Fuzzy Inference System and a Type 2 Fuzzy Logic Inference System to select the operators of a Genetic Algorithm during execution time to solve a set of ten test functions from the literature. The results of computational experiments show that the fuzzy selection of operators improves the performance of the original GA on nine of the ten test functions with practically the same execution time.

Published
2018-01-08
How to Cite
López-Locés, M., Fraire-Huacuja, H., Pazos Rangel, R., González Barbosa, J., & Terán Villanueva, J. (2018). Self-adjusting a Genetic Algorithm Using Fuzzy Logic Techniques. International Journal of Combinatorial Optimization Problems and Informatics, 8(1), 6-11. Retrieved from https://ijcopi.org/index.php/ojs/article/view/2
Section
Articles