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Fast training of support vector machines using sequential minimal optimization
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Evolving Teams of Predictors with Linear Genetic Programming
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Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Large-Scale Kernel Machines (Neural Information Processing)
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Training binary GP classifiers efficiently: a Pareto-coevolutionary approach
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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
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
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IEEE Transactions on Evolutionary Computation
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Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
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Genetic Programming and Evolvable Machines
Label free change detection on streaming data with cooperative multi-objective genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.