Novelty search for global optimization

Iztok Fister

doi:10.1016/j.amc.2018.11.052

Novelty search for global optimization

2019-04-01

42

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AI

DOI：10.1016/j.amc.2018.11.052

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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. (C) 2018 Elsevier Inc. All rights reserved.

Keyword:

Novelty search

Differential evolution

Swarm intelligence

Evolutionary robotics

Artificial life

Novelty search for global optimization

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