Application of Self-adapting Genetic Algorithms to Generate Fuzzy Systems for a Regression Problem

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.