Competitive Coevolution through Evolutionary Complexification

@article{Stanley2004CompetitiveCT,
  title={Competitive Coevolution through Evolutionary Complexification},
  author={Kenneth O. Stanley and Risto Miikkulainen},
  journal={ArXiv},
  year={2004},
  volume={abs/1107.0037}
}
Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain… 

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