Novelty search is a powerful biologically motivated method for discovering successful behaviors especially in deceptive domains, like those in artificial life. This paper extends the biological motivation further by distributing novelty search to run in parallel in multiple islands, with periodic migration among them. In this manner, it is possible to scale novelty search to larger populations and more diverse runs, and also to harness available computing power better. A second extension is to improve novelty searchs ability to solve practical problems by biasing the migration and elitism towards higher fitness. The resulting method, DANS, is shown to find better solutions much faster than pure single-population novelty search, making it a promising candidate for solving deceptive design problems in the real world.

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© 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2016
MIT Press
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