A fascinating aspect of natural evolution is its ability to produce a diversity of organisms that are all high performing in their niche. By contrast, the main artificial evolution algorithms are focused on pure optimization, that is, finding a single high-performing solution.

Quality-Diversity optimization (or illumination) is a new type of evolutionary algorithm that aims at generating large collections of diverse solutions that are all high-performing. This concept was introduced by the Generative and Developmental Systems community between 2011 (Lehman & Stanley, 2011) and 2015 (Mouret & Clune, 2015) with the Novelty Search with Local Competition and MAP-Elites evolutionary algorithms. The main differences with multi-modal optimization algorithms are that (1) Quality Diversity typically works in the behavioral space (or feature space), and not in the genotypic space, and (2) Quality Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In less than 4 years, about 70 papers have been written about quality diversity, many of them in the GECCO community (a non exhaustive list is available here).

The collections of solutions obtained by Quality Diversity algorithms open many new applications for evolutionary computation. In robotics, it was used to create repertoires of behaviors (Cully & Mouret, 2016), to allow robots to adapt to damage in a few minutes (Cully & et al. 2015); in engineering, it can be used to propose a diversity of optimized aerodynamic shapes (Gaier & et al., 2018); they were also recently used in video games (Khalifa & et al., 2018) and for Workforce Scheduling and Routing Problems (WSRP) (Urquhart & Hart, 2018).

Main Contributors

The list of papers has been completed with references from the list made by Daniele Gravina


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