Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Dynamic Subset Selection Based on a Fitness Case Topology
Evolutionary Computation
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
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The conversion and extension of the Incremental Pareto-Coevolution 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 1430 of the wall-clock time.