The optimization of functions to find the best solution according to one or
several objectives has a central role in many engineering and research fields.
Recently, a new family of optimization algorithms, named Quality-Diversity
optimization, has been introduced, and contrasts with classic algorithms.
Instead of searching for a single solution, Quality-Diversity algorithms are
searching for a large collection of both diverse and high-performing solutions.
The role of this collection is to cover the range of possible solution types as
much as possible, and to contain the best solution for each type. The
contribution of this paper is threefold. Firstly, we present a unifying
framework of Quality-Diversity optimization algorithms that covers the two main
algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and
the Novelty Search with Local Competition), and that highlights the large
variety of variants that can be investigated within this family. Secondly, we
propose algorithms with a new selection mechanism for Quality-Diversity
algorithms that outperforms all the algorithms tested in this paper. Lastly, we
present a new collection management that overcomes the erosion issues observed
when using unstructured collections. These three contributions are supported by
extensive experimental comparisons of Quality-Diversity algorithms on three
different experimental scenarios.

本文提出了一个全面的质量多样性优化算法统一框架，探讨了该算法族群的大量变体，并提出了一种新的集合管理机制解决了在使用无结构的集合时观察到的侵蚀问题，同时还提出了使用质量多样性优化算法的新的选择机制，其性能比本文测试的所有算法都要好，这三个贡献都得到了质量多样化算法在三个不同实验场景下的广泛实验比较的支持。