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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, 2019, 347, 865-881
Publisher: Elsevier Inc.
Publication date: 01/04/2019
No. of pages: 17
Publication type: Article
DOI: 10.1016/j.amc.2018.11.052
ISSN: 0096-3003,1873-5649
Spanish project: TIN2017-89275-R
Publication Url: https://doi.org/10.1016/j.amc.2018.11.052
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FISTER, IZTOK
ANDRES IGLESIAS PRIETO
AKEMI GALVEZ TOMIDA
DEL SER, JAVIER
OSABA, ENEKO
FISTER, IZTOK JR.
PERC, MATJAŽ
SLAVINEC, MITJA
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