Fitness inheritance in multi-objective genetic algorithms: a case study on fuzzy classification rule mining

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Research Article

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International Journal of Advanced Intelligence Paradigms


The trade-off between accuracy and interpretability in fuzzy rule-based classifier has been examined in this paper by incorporating fitness inheritance in multi-objective genetic algorithms. The aim of this mechanism is to reduce the number of fitness evaluation by estimating the fitness value of the offspring individual from the fitness value of their parents. The multi-objective genetic algorithms with efficiency enhancement technique are a hybrid version of Michigan and Pittsburgh approaches. Fuzzy rules are represented by its antecedent fuzzy sets as an integer string of fixed length and a concatenated integer string of variable length. Our approach simultaneously maximises the accuracy of rule sets and minimises their complexity (i.e., maximisation of interpretability). As a result of adopting fitness inheritance, it minimises the overall time to generate rule set. The experimental outcome confirms that the proposed method reduces the computational cost, without compromising the quality of the results in a significant way.

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