Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Behavioral Diversity and a Probabilistically Optimal GP Ensemble
Genetic Programming and Evolvable Machines
N-version genetic programming: a probabilistically optimal ensemble approach
N-version genetic programming: a probabilistically optimal ensemble approach
Generating Multiple Diverse Software Versions with Genetic Programming
EUROMICRO '98 Proceedings of the 24th Conference on EUROMICRO - Volume 1
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
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
Training time and team composition robustness in evolved multi-agent systems
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Teams of genetic predictors for inverse problem solving
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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Evolution of multi-agent teams has been shown to be an effective method of solving complex problems involving the exploration of an unknown problem space. These autonomous and heterogeneous agents are able to go places where humans are unable to go and perform tasks that would be otherwise dangerous or impossible to complete. This research tests the ability of the Orthogonal Evolution of Teams (OET) algorithm to evolve heterogeneous teams of agents which can change their composition, i.e. the numbers of each type of agent on a team. The results showed that OET could effectively produce both the correct team composition and a team for that composition that was competitive with teams evolved with OET where the composition was fixed a priori