Abstract
Nowadays, automatic learning of fuzzy rule-based systems is being addressed as a multi-objective optimization problem. A new research area of multi-objective genetic fuzzy systems (MOGFS) has capture the attention of the fuzzy community. Despite the good results obtained, most of existent MOGFS are based on a gross usage of the classic multi-objective algorithms. This paper takes an existent MOGFS and improves its convergence by modifying the underlying genetic algorithm. The new algorithm is tested in a set of real-world regression problems with successful results.
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González, M., Casillas, J., Morell, C. (2010). SIFT-SS: An Advanced Steady-State Multi-Objective Genetic Fuzzy System. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_1
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DOI: https://doi.org/10.1007/978-3-642-13803-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
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