Doncieux, Stephane, and Alexandre Coninx. “Open-ended evolution with multi-containers QD.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion , pp. 107-108. 2018.
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Evolution in nature has allowed biological systems to develop and survive in many different environments. It can be attributed to one of the major features of natural evolution: its open-endedness, that can be considered as the ability to continuously produce novelty and/or complexity [1]. This feature is critical to allow an agent to continuously adapt to its environment. We propose here an extension of Quality Diversity algorithms to make it more open-ended. Quality Diversity algorithms aim at generating a large set of diverse solutions [3, 10]. They can be used in a two-steps process in which (1) a diverse set of solutions is generated offline before (2) the agent can exploit it online to find the behavior that fits to the situation [2, 4–6]. QD algorithms main goal is to fill a behavior space whose dimensions are defined by behavior descriptors. Current QD algorithms can generate novelty and complexity, but within the frame of these behavior descriptors. To make the evolutionary process more open-ended, we extend Cully and Demiris framework [3] and introduce Multi-Container Quality Diversity algorithms (MCQD). MCQD are QD algorithms relying on multiple behavior spaces that can be added on the fly. MCQD thus aims at implementing an open-ended evolutionary process that would explore any new behavior space that could be found of interest during the search process.