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.

Iztok Fister acknowledges financial support from the Slovenian Research Agency (Grant no. P2-0041). Iztok Fister Jr. acknowledges financial support from the Slovenian Research Agency (Grant no. P2-0057). Matjaž Perc acknowledges financial support from the Slovenian Research Agency (Grant nos. J1-7009, J4-9302, J1-9112 and P5-0027). Andres Iglesias and Akemi Galvez acknowledge financial support from the projects TIN2017-89275-R (AEI/FEDER, UE) and PDE-GIR (H2020, MSCA program, ref. 778035). Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program.