Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification
نویسندگان
چکیده
| In this paper, we use a genetic algorithm to evolve a set of classiication rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classiication as an optimization problem, and evolve rule sets that maximize the number of correct classiications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel connict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the eeectiveness of our proposed approach on a benchmark wine classiier system.
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