Basque Optimization: a new cost function prediction based optimization algorithm

Abstract

Abstract Authors propose a new intelligent optimization algorithm. This algorithm tries to learn the cost function shape in order to decide which points must be evaluated, and how many optimization iterations are enough. As far as the authors know, there is no optimization algorithm that applies prediction with all the evaluated points. Authors have performed a comparison study of the error prediction made by both the proposed algorithm, and the best-known intelligent optimization algorithm: Particle Swarm Optimization. The results show that this new algorithm is able to learn different cost functions more accurately. The cost function set proposed in this article are continuous evaluated functions which have very diverse mathematical shapes. The authors have concluded that the proposed algorithm is able to choose the evaluation points more appropriately.