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Abstract

This work presents a new model for the automatic synthesis of fuzzy classifiers, based on quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the use of hybrid representations and the treatment of multiple objectives, both necessary for the synthesis of these types of systems. Without any a priori information about the classifier rules or any initial adjustment of individuals, the results obtained are comparable to those of other techniques that start from classifier populations previously adjusted to obtain good performance. According to the current trend, the aim was to build classifiers with good accuracy and simultaneous high interpretability of their fuzzy rule base.

Keywords

Fuzzy classifier quantum-inspired evolutionary algorithms multi-objective optimization

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Quantum-inspired evolutionary multi-objective fuzzy classifier with real and categorical representation

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