Six variants of self-adapting genetic algorithms with varying mutation, crossover, and selection were developed. To implement selfadaptation 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 N×N comparisons.
CITATION STYLE
Lasota, T., Smętek, M., Telec, Z., Trawiński, B., & Trawiński, G. (2014). Application of self-adapting genetic algorithms to generate fuzzy systems for a regression problem. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8733, 49–61. https://doi.org/10.1007/978-3-319-11289-3_6
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