Quality-diversity algorithms address the challenges of navigating low-performing valleys and escaping local optima by incorporating novelty search and behavioral diversity into the optimization process of genetic algorithms. The multi-dimensional archive of phenotypic elites 10 (MAP-Elites) is the premier qualitydiversity algorithm. It rewards novelty and enforces diversity by replacing the evolutionary population of a genetic algorithm with a multi-dimensional archive containing the best solutions found so far in each niche of a pre-defined search space. MAP-Elites produces more diverse and higher performing populations throughout its optimisation run compared to genetic algorithms, providing the algorithm with crucial stepping stones which allow it to traverse low-performing valleys and bypass local optima.