Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
N-Version Design Versus One Good Version
IEEE Software
Behavioral Diversity and a Probabilistically Optimal GP Ensemble
Genetic Programming and Evolvable Machines
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Cooperative evolution on the intertwined spirals problem
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th 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
Training time and team composition robustness in evolved multi-agent systems
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
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
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There are two common methods of evolving teams of genetic programs. Research suggests Island approaches produce teams of strong individuals that cooperate poorly and Team approaches produce teams of weak individuals that cooperate strongly. Ideally, teams should be composed of strong individuals that cooperate well. In this paper we present a new class of algorithms called Orthogonal Evolution of Teams (OET) that overcomes the weaknesses of current Island and Team approaches by applying evolutionary pressure at both the level of teams and individuals during selection and replacement. We present four novel algorithms in this new class and compare their performance to Island and Team approaches as well as multi-class Adaboost on a number of classification problems.