Curious-II: A Multi/Many-Objective Optimization Algorithm with Subpopulations based on Multi-novelty Search

Novelty search’s ability to efficiently explore the fitness space is gaining attention. Different novelty metrics, however, produce different search results. Here we show that novelty metrics are complementary and a multi-novelty approach improves the performance substantially. Specifically, we propose a multi-novelty search multi/many-objective algorithm (Curious II) that has both Euclidian distance and prediction-error novelty metrics. On the one hand, the Euclidian distance based novelty metric makes the subpopulation explore subspaces with low crowd density and avoids premature convergence. On the other hand, the prediction-error novelty metric guides a subpopulation to explore subspaces with unexpected objective fitness. Experiments reveal that using both novelty metrics in a multi-novelty algorithm has strong benefits. Curious II was compared with two state-of-the-art algorithms and two novelty search-based algorithms on the WFG 1-8 test problem with up to 10 objectives. It outperforms all the others in 28 out of 32 tasks for the HV index and in 27 out of 32 tasks for the IGD index.