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.
@article{urn:eudml:doc:39043,
title = {The use of fuzzy connectives to design real-coded genetic algorithms.},
journal = {Mathware and Soft Computing},
volume = {1},
year = {1994},
pages = {239-251},
zbl = {0833.68054},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39043}
}
Herrera, Francisco; Lozano, Manuel; Verdegay, José Luis. The use of fuzzy connectives to design real-coded genetic algorithms.. Mathware and Soft Computing, Tome 1 (1994) pp. 239-251. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39043/