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• W1010268253 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 N×N comparisons." @default.
• W1010268253 created "2016-06-24" @default.
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• W1010268253 date "2014-01-01" @default.
• W1010268253 modified "2023-10-18" @default.
• W1010268253 title "Application of Self-adapting Genetic Algorithms to Generate Fuzzy Systems for a Regression Problem" @default.
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• W1010268253 doi "https://doi.org/10.1007/978-3-319-11289-3_6" @default.
• W1010268253 hasPublicationYear "2014" @default.
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