Pareto-coevolutionary genetic programming classifier

The conversion and extension of the Incremental ParetoCoevolution Archive algorithm (IPCA) into the domain of Genetic Programming classifier evolution is presented. In order to accomplish efficiency in regards to classifier evaluation on training data, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. The algorithm is compared in terms of classification "score" (equal weight to detection rate, and 1 - false positive rate), and run-time against a traditional GP classifier using the entirety of the training data for evaluation, and a GP classifier which performs Dynamic Subset Selection. The results indicate that the presented algorithm outperforms the subset selection algorithm in terms of classification score, and outperforms the traditional classifier while requiring roughly 1 430 of the wall-clock time. Categories and Subject Descriptors I.2.6 [Artific...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Michal Lemczyk, Malcolm I. Heywood
• Keywords: Algorithm, GECCO 2006, Optimization, Subset Selection, Training Data