Paper Info
Title
Application Of Self-Adapting Genetic Algorithms To Generate Fuzzy Systems For A Regression Problem
Abstract
Six variants of self-adapting genetic algorithms with varying mutation, crossover, and selection were developed. To implement self-adaptation the main part of a chromosome which comprised the solution was extended to include mutation rates, crossover rates, and/or tournament size. The solution part comprised the representation of a fuzzy system and was real-coded whereas to implement the proposed self-adapting mechanisms binary coding was employed. The resulting self-adaptive genetic fuzzy systems were evaluated using real-world datasets derived from a cadastral system and included records referring to residential premises transactions. They were also compared in respect of prediction accuracy with genetic fuzzy systems optimized by a classical genetic algorithm, multilayer perceptron and radial basis function neural network. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple NxN comparisons.
Year
DOI
Venue
2014
10.1007/978-3-319-11289-3_6
COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, ICCCI 2014
Keywords
Field
DocType
self-adaptive GA, mutation, crossover, genetic fuzzy systems
Data mining,Computer science,Fuzzy set operations,Fuzzy transportation,Multilayer perceptron,Artificial intelligence,Fuzzy control system,Quality control and genetic algorithms,Genetic algorithm,Crossover,Algorithm,Genetic fuzzy systems,Machine learning
Conference
Volume
ISSN
Citations 
8733
0302-9743
0
PageRank 
References 
Authors
0.34
30
5
Name
Order
Citations
PageRank
Tadeusz Lasota134825.33
Magdalena Smȩtek2764.12
Zbigniew Telec317014.92
Bogdan Trawinski411512.89
Grzegorz Trawiński5474.81