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Novelty search for global optimization

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.

 Fuente: Applied Mathematics and Computation Volume 347, 15 April 2019, Pages 865-881

Editorial: Elsevier Inc.

 Fecha de publicación: 01/04/2019

Nº de páginas: 17

Tipo de publicación: Artículo de Revista

DOI: 10.1016/j.amc.2018.11.052

ISSN: 0096-3003,1873-5649

Proyecto español: TIN2017-89275-R

Url de la publicación: https://doi.org/10.1016/j.amc.2018.11.052

Autores/as

FISTER, IZTOK

DEL SER, JAVIER

OSABA, ENEKO

FISTER, IZTOK JR.

PERC, MATJAŽ

SLAVINEC, MITJA