Using group selection to evolve leadership in populations of self-replicating digital organisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Selection for group-level efficiency leads to self-regulation of population size
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hierarchical cooperative evolutionary algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Evolving cooperation by evolutionary algorithms is impossible without introducing extra mechanisms. Group selection theory in biology is a good candidate as it explains the evolution of cooperation in nature. Two biological models, Wilson's trait group selection model and Traulsen's group selection model are investigated and compared in evolutionary computation. Three evolutionary algorithms were designed and tested on an n-player prisoner's dilemma problem; two EAs implement the original Wilson and Traulsen models respectively, and one EA extends Traulsen's model. Experimental results show that the latter model introduces high between-group variance, leading to more robustness than the other two in response to parameter changes such as group size, the fraction of cooperators and selection pressure.