Using real-valued genetic algorithms to evolve rule sets for classification

In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges could be encoded with real-valued genes and present a new uniform method for representing don´t cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system