- Evolution, Reinforcement learning, Search algorithms
- (Pugh, Soros, and Stanley 2016; Cully and 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.
- Cully, Antoine, and Yiannis Demiris. 2017. "Quality and Diversity Optimization: A Unifying Modular Framework". IEEE Transactions on Evolutionary Computation 22 (2). IEEE:245–59.
- Pugh, Justin K., Lisa B. Soros, and Kenneth O. Stanley. 2016. “Quality Diversity: A New Frontier for Evolutionary Computation”. Frontiers in Robotics and AI 3. Frontiers.