Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Massively parallel genetic programming
Advances in genetic programming
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
Ensemble techniques for parallel genetic programming based classifiers
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
GP classification under imbalanced data sets: active sub-sampling and AUC approximation
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Cooperative problem decomposition in Pareto competitive classifier models of coevolution
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Symbiosis, complexification and simplicity under GP
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
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The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets. Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.