Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Co-evolving Soccer Softbot Team Coordination with Genetic Programming
RoboCup-97: Robot Soccer World Cup I
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Novel ways of improving cooperation and performance in ensemble classifiers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Teams of genetic predictors for inverse problem solving
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Environmental robustness in multi-agent teams
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolution of team composition in multi-agent systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A developmental approach to evolving scalable hierarchies for multi-agent swarms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Evolutionary algorithms are effective at creating cooperative, multi-agent systems. However, current Island and Team algorithms show subtle but significant weaknesses when it comes to balancing member performance with member cooperation, leading to sub-optimal overall team performance. In this paper we apply a new class of cooperative multi-agent evolutionary algorithms called Orthogonal Evolution of Teams (OET) which produce higher levels of cooperation and specialization than current team algorithms. We also show that sophisticated behavior evolves much sooner using OET algorithms, even when training resources are significantly reduced. Finally, we show that when teams must be reformed, due to agent break down for example, those teams composed of individuals sampled from OET teams perform much better than teams composed of individuals sampled from teams created by other methods.