Abstract
This paper presents a distributed genetic algorithm for the discovery of classification rules. Population is contained in the form of interconnected demes. The local selection and reproduction mechanism is used to evolve the species within demes, and diversity is enhanced by migrating rules among some of the selected demes. Subsumption operator has been finally applied to reduce the complexity of the rule set discovered. The effectiveness of the proposed distributed genetic algorithm for discovering classification rules is evaluated by comparing the results with traditional crowding GA on 10 datasets from the UCI and KEEL repository. The results confirm that the distributed GA discover classification rules with significantly higher predictive accuracy. The influence of migration operator is also analysed with respect to migration rate.
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