While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively in competitive problem-solving domains. This paper formalizes human creative problem solving as competitive multi-agent search, and advances the hypothesis that evolutionary computation can be used to discover effective strategies for it. In experiments in a social innovation game (similar to a fantasy sports league), neural networks were first trained to model individual human players. These networks were then used as opponents to evolve better game-play strategies with the NEAT neuroevolution method. Evolved strategies scored significantly higher than the human models by innovating, retaining, and retrieving less and by imitating more, thus providing insight into how performance could be improved in such domains. Evolutionary computation in competitive multi-agent search thus provides a possible framework for understanding and supporting various human creative activities in the future.

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In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain, 2015.

@inproceedings{bahceci:gecco15, title={Evolving Strategies for Social Innovation Games}, author={Erkin Bahceci and Riitta Katila and Risto Miikkulainen}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)}, address={Madrid, Spain}, url="http://nn.cs.utexas.edu/?bahceci:gecco15", year={2015} }