Novelty search for global optimization

Iztok Fister, Andres Iglesias, Akemi Galvez, Javier Del Ser, Eneko Osaba, Iztok Fister, Matjaž Perc and Mitja Slavinec

Applied Mathematics and Computation, 2019, vol. 347, issue C, 865-881

Abstract: 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.

Keywords: Novelty search; Differential evolution; Swarm intelligence; Evolutionary robotics; Artificial life (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:347:y:2019:i:c:p:865-881

DOI: 10.1016/j.amc.2018.11.052

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