Phenotypic Niching Using Quality Diversity Algorithms

Here we describe quality diversity algorithms, a recent and powerful class of evolutionary algorithms that produces a diverse set of high-performing solutions. The optimization paradigm emphasizes phenotypic niching and egalitarian treatment of quality and diversity. We ground quality diversity in ecology, describe the historical development, and give an intuition and formalization of the algorithms. We present a practical example that we refer to for engineers and laymen readers to understand how and why quality diversity can be used. The main insights from research of quality diversity, performance metrics, and benchmarks are discussed. Finally, the open challenges are presented.