Implementation of evolutionary fuzzy systems
TLDR
Benefits of the methodology are illustrated in the process of classifying the iris data set and possible extensions of the methods are summarized.Abstract:
Evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.read more
Citations
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Book
Genetic Programming: An Introduction
TL;DR: The authors discuss GP software tools, including Discipulus, the GP software developed by the authors, and Appendix D mentions events most closely related to the field of genetic programming.
Proceedings ArticleDOI
Fuzzy adaptive particle swarm optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.
Book
Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
TL;DR: Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms and Other Kinds of Evolutionary Fuzzies Applications.
Proceedings ArticleDOI
Particle swarm optimization method in multiobjective problems
TL;DR: Critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems.
Journal ArticleDOI
Genetic fuzzy systems: taxonomy, current research trends and prospects
TL;DR: An overview of the field of GFSs, with a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process, and some potential future research directions.
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Journal ArticleDOI
Optimization of Control Parameters for Genetic Algorithms
TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.