Michigan-style Fuzzy GBML with (1+1)-ES Generation Update and Multi-Pattern Rule Generation

A variety of fuzzy genetics-based machine learning algorithms have been proposed in the frameworks of Michigan and Pittsburgh approaches. Since each individual is a single rule, Michigan-style algorithms need much less computation time than Pittsburgh-style algorithms where each individual is a rule set. For the same reason, Michigan-style algorithms cannot directly optimize rule sets. Rule set optimization is indirectly performed by optimizing each rule. In this paper, we propose the use of the (1+1)-ES generation update in Michigan-style algorithms. This is for directly performing rule set optimization without losing their high computational efficiency. We also propose a multi-pattern-based rule generation method to generate a fuzzy rule from multiple patterns in a heuristic manner. We demonstrate high efficiency and high generalization ability of our newly proposed Michigan-style algorithm through computational experiments on 19 data sets with 4-310 attributes and 2-15 classes.