Novelty Search

Novelty search is a computational approach focused on discovering diverse and unusual solutions to problems, rather than solely optimizing for a single best solution. Current research explores its application across diverse fields, employing techniques like Bayesian optimization, reinforcement learning, and evolutionary algorithms (including genetic algorithms and neuroevolution) to guide the search for novelty in areas such as recommendation systems, planning, and test case generation. This methodology holds significant promise for advancing fields like materials science, drug discovery, and AI safety by enabling the exploration of a wider range of potential solutions and mitigating the risk of getting trapped in suboptimal local optima.