- Evolution, Reinforcement learning, Search algorithms
- (Pugh et al. 2016; Cully, Demiris 2017)
QD is about creating algorithms that favor diversity in searching the space. In QD, one needs to both:
- Measure the quality of a solution
- Have a way to describe the effect of a solution
Solutions in QD have to be good in the two above ways.
QD is also a form of novelty search.
- Justin K. Pugh, Lisa B. Soros, Kenneth O. Stanley. . "Quality diversity: A new frontier for evolutionary computation". Frontiers in robotics and AI 3. Frontiers. DOI.
- Antoine Cully, Yiannis Demiris. . "Quality and Diversity Optimization: A Unifying Modular Framework". IEEE Transactions on Evolutionary Computation 22 (2). IEEE:245–59.