Type inheritance in strongly typed genetic programming
Advances in genetic programming
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Proceedings of the 9th annual conference on Genetic and evolutionary computation
New methods for competitive coevolution
Evolutionary Computation
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Coevolution of heterogeneous multi-robot teams
Proceedings of the 12th 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
A multiagent approach to managing air traffic flow
Autonomous Agents and Multi-Agent Systems
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
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Coevolution is a natural approach to evolve teams of agents which must cooperate to achieve some system objective. However, in many coevolutionary approaches, credit assignment is often subjective and context dependent, as the fitness of an individual agent strongly depends on the actions of the agents with which it collaborates. In order to alleviate this problem, we introduce a cooperative coevolutionary algorithm which biases the evolutionary search as well as shapes agent fitness functions to reward behavior that benefits the system. More specifically, we bias the search using a hall of fame approximation of optimal collaborators, and we shape the agent fitness using the difference evaluation function. Our results show that shaping agent fitness with the difference evaluation improves system performance by up to 50%, and adding an additional fitness bias can improve performance by up to 75%.