Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Novel ways of improving cooperation and performance in ensemble classifiers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Evolutionary Computation
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Natural niching for evolving cooperative classifiers
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A hierarchical cooperative evolutionary algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Symbiosis, complexification and simplicity under GP
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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IEEE Transactions on Evolutionary Computation
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Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Genetic Programming and Emergence
Genetic Programming and Evolvable Machines
Emergence in genetic programming
Genetic Programming and Evolvable Machines
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This paper aims to improve the capability of genetic programming to tackle the evolution of cooperation: evolving multiple partial solutions that collaboratively solve structurally and functionally complex problems. A multilevel genetic programming approach is presented based on a new computational multilevel selection framework [19]. This approach considers biological group selection theory to encourage cooperation, and a new cooperation operator to build solutions hierarchically. It extends evolution from individuals to multiple group levels, leading to good performance on both individuals and groups. The applicability of this approach is evaluated on 7 multi-class classification problems with different features, such as non-linearity, skewed data distribution and large feature space. The results, when compared to other cooperative evolutionary algorithms in the literature, demonstrate that this approach improves solution accuracy and consistency, and simplifies solution complexity. In addition, the problem is decomposed as a result of evolution without human interference.