Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
Cheating for problem solving: a genetic algorithm with social interactions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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This paper describes a new approach for building evolutionary optimisation algorithms inspired by concepts borrowed from evolution of social behaviour. The proposed approach utilises a set of behaviours used as operators that work on a population of individuals. These behaviours are used and evolved by groups of individuals to enhance a group adaptation to the environment and to other groups. Each group has two sets of behaviours: one for intra-group interactions and one for inter-group interactions. These behaviours are evolved using mathematical models from the field of evolutionary game theory. This paper describes the proposed paradigm and starts studying its characteristics by building a new evolutionary algorithm and studying its behaviour. The algorithm has been tested using a benchmark problem generator with promising initial results, which are also reported. We conclude the paper by identifying promising directions for the continuation of this research.