Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: SSCI 2015, IEEE Symposium Series on Computational Intelligence; Cape Town, South Africa; Cape Town, South Africa
Год издания: 2015
Ключевые слова: fuzzy set theory, genetic algorithms, search problems, fuzzy rule based classifier design, hybridization, Machine Learning Techniques, Multimodal optimization, multistrategy multimodal genetic algorithm, Computational intelligence real-world classification problems search metaheuristic controls self-configuring genetic algorithm
Аннотация: A hybridization of genetic algorithms and machine learning techniques have proved its effectiveness for many complex benchmark and real-world problems. In this study we present a novel approach that combines self-configuring genetic algorithm for multimodal optimization and fuzzy rule based classifier. The proposed search metaheuristic controls the interactions of many techniques for multimodal optimization (different genetic algorithms) and leads to the self-configuring solving of problems with a priori unknown structure. Appling this approach to designing the fuzzy rule based classifiers, we can obtain many optimal solutions with different representation. The results of numerical experiments with popular optimization benchmark problems (for multimodal genetic algorithm) and with well-studied real-world classification problems (for self-configuring fuzzy rule based classifier design) are presented and discussed. The main feature of the proposed approach is that it does not require the participation of the human expert, because it operates in an automated, self-configuring way.
Журнал: 2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015)
Номера страниц: 167-173
Издатель: Institute of Electrical and Electronics Engineers
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