Species Separation by a Clustering Mean towards Multimodal Function Optimization
Cătălin Stoean, Mike Preuß, Ruxandra Stoean · Leiden Repository (Leiden University) · 2009
Abstract. Present paper introduces a new evolutionary technique for multimodal real-valued optimization which uses a clustering method for separating the individuals within a population into species that are each connected to different optima from the search space. It is applied for a set of benchmark functions both for uni- and multimodal optimization and it proves to be very efficient as regards both the accuracy of the obtained results and the costs regarding the fitness evaluation calls that are spent.