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

View/Open
Document typeArticle
Defense date1994
PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
:
Attribution-NonCommercial-NoDerivs 3.0 Spain
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.
ISSN1134-5632
Collections
Files | Description | Size | Format | View |
---|---|---|---|---|
herrera.pdf | 389,8Kb | View/Open |