%0 Journal Article
%A Fister, Iztok
%A Iglesias, Andres
%A Galvez, Akemi
%A Del Ser, Javier
%A Osaba, Eneko
%A Fister, Iztok
%A Perc, Matjaž
%A Slavinec, Mitja
%T Novelty search for global optimization
%D 2019
%@ 0096-3003
%U https://hdl.handle.net/11556/4511
%X 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.
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