The use of fuzzy connectives to design real-coded genetic algorithms.

Herrera, Francisco, Lozano, Manuel, and Verdegay, José Luis. "The use of fuzzy connectives to design real-coded genetic algorithms.." Mathware and Soft Computing 1.3 (1994): 239-251. <http://eudml.org/doc/39043>.

@article{Herrera1994,
abstract = {Genetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is premature convergence; the search becomes trapped in a local optimum before the global optimum is found. Fuzzy logic techniques may be used for solving this problem. This paper presents one of them: the design of crossover operators for real-coded genetic algorithms using fuzzy connectives and its extension based on the use of parameterized fuzzy connectives as tools for tackling the premature convergence problem.},
author = {Herrera, Francisco, Lozano, Manuel, Verdegay, José Luis},
journal = {Mathware and Soft Computing},
keywords = {Algoritmos genéticos; Lógica difusa; Código genético; Procesos de decisión; genetic algorithms; fuzzy connectives},
language = {eng},
number = {3},
pages = {239-251},
title = {The use of fuzzy connectives to design real-coded genetic algorithms.},
url = {http://eudml.org/doc/39043},
volume = {1},
year = {1994},
}

TY - JOUR
AU - Herrera, Francisco
AU - Lozano, Manuel
AU - Verdegay, José Luis
TI - The use of fuzzy connectives to design real-coded genetic algorithms.
JO - Mathware and Soft Computing
PY - 1994
VL - 1
IS - 3
SP - 239
EP - 251
AB - Genetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is premature convergence; the search becomes trapped in a local optimum before the global optimum is found. Fuzzy logic techniques may be used for solving this problem. This paper presents one of them: the design of crossover operators for real-coded genetic algorithms using fuzzy connectives and its extension based on the use of parameterized fuzzy connectives as tools for tackling the premature convergence problem.
LA - eng
KW - Algoritmos genéticos; Lógica difusa; Código genético; Procesos de decisión; genetic algorithms; fuzzy connectives
UR - http://eudml.org/doc/39043
ER -