Novelty-based restarts for evolution strategies

A major limitation in applying evolution strategies to black box optimization is the possibility of convergence into bad local optima. Many techniques address this problem, mostly through restarting the search. However, deciding the new start location is nontrivial since neither a good location nor a good scale for sampling a random restart position are known. A black box search algorithm can nonetheless obtain some information about this location and scale from past exploration. The method proposed here makes explicit use of such experience, through the construction of an archive of novel solutions during the run. Upon convergence, the most "novel" individual found so far is used to position the new start in the least explored region of the search space, actively looking for a new basin of attraction. We demonstrate the working principle of the method on two multi-modal test problems.