Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Cooperative Mobile Robotics: Antecedents and Directions
Autonomous Robots
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
Collective intelligence and bush fire spotting
Proceedings of the 10th 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
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
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Evolution has proven to be an effective method of training heterogeneous multi-agent teams of autonomous agents to explore unknown environments. Autonomous, 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. However, a serious problem for practical applications of multi-agent teams is how to move from training environments to real-world environments. In particular, if the training environment cannot be made identical to the real-world environment how much will performance suffer? In this research we investigate how differences in training and testing environments affect performance. We find that while in general performance degrades from training to testing, for difficult training environments performance improves in the test environment. Further, we find distinct differences between the performance of different training algorithms with Orthogonal Evolution of Teams (OET) producing the best overall performance.