Intelligent data mining via evolutionary computing

Abstract

This thesis proposes two rule-based classification algorithms, in which the first one is a two-phase evolutionary approach and the second is a distributed coevolutionary classifier. The classification performances and the efficiency of the evolution process are the two major considerations of the both algorithms. In the two-phase approach, a hybrid evolutionary algorithm is utilized in the first phase to confine the search space by evolving a pool of good candidate rules. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. While in the coevolutionary classifier, by utilizing the existing Internet and hardware resources, distributed computing is naturally incorporated into the coevolutionary algorithm to enhance its concurrent processing and performance. Through the inter-communications between the different species, the cooperation is conducted in a more effective and efficient way.