Fister, IztokIglesias, AndresGalvez, AkemiDel Ser, JavierOsaba, EnekoFister, IztokPerc, MatjažSlavinec, Mitja2024-07-242024-07-242019-04-15Fister , I , Iglesias , A , Galvez , A , Del Ser , J , Osaba , E , Fister , I , Perc , M & Slavinec , M 2019 , ' Novelty search for global optimization ' , Applied Mathematics and Computation , vol. 347 , pp. 865-881 . https://doi.org/10.1016/j.amc.2018.11.0520096-3003https://hdl.handle.net/11556/4511Publisher Copyright: © 2018 Elsevier Inc.Novelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization.17enginfo:eu-repo/semantics/restrictedAccessjournal article10.1016/j.amc.2018.11.052Artificial lifeDifferential evolutionEvolutionary roboticsNovelty searchSwarm intelligenceComputational MathematicsApplied Mathematicshttp://www.scopus.com/inward/record.url?scp=85057867913&partnerID=8YFLogxK