Browsing by Keyword "Novelty search"
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Item Novelty search for global optimization(2019-04-15) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Fister, Iztok; Perc, Matjaž; Slavinec, Mitja; IA; QuantumNovelty 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.Item Using novelty search in differential evolution(Springer Verlag, 2018) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Corchado, Juan M.; Julian, Vicente; Osaba Icedo, Eneko; Bajo, Javier; Hoffa-Dabrowska, Patrycja; Silveira, Ricardo Azambuja; Fernandez, Alberto; Giroux, Sylvain; Navarro Martínez, Elena María; Mathieu, Philippe; Castro, Antonio J.; Sanchez-Pi, Nayat; del Val, Elena; Unland, Rainer; Fuentes-Fernandez, Ruben; IA; QuantumNovelty search in evolutionary robotics measures a distance of potential novelty solutions to their k-nearest neighbors in the search space. This distance presents an additional objective to the fitness function, with which each individual in population is evaluated. In this study, the novelty search was applied within the differential evolution. The preliminary results on CEC-14 Benchmark function suite show its potential for using also in the future.