We developed two GA-based schemes for the design of fuzzy rule-based classification systems. One is genetic rule selection and the other is genetics-based machine learning (GBML). In our genetic rule selection scheme, first a large number of promising fuzzy rules are extracted from numerical data in a heuristic manner as candidate rules. Then a genetic algorithm is used to select a small number of fuzzy rules. A rule set is represented by a binary string whose length is equal to the number of candidate rules. On the other hand, a fuzzy rule is denoted by its antecedent fuzzy sets as an integer substring in our GBML scheme. A rule set is represented by a concatenated integer string. In this paper, we compare these two schemes in terms of their search ability to efficiently find compact fuzzy rule-based classification systems with high accuracy. The main difference between these two schemes is that GBML has a huge search space consisting of all combinations of possible fuzzy rules while genetic rule selection has a much smaller search space with only candidate rules.
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