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Novelty search for global optimization

Author

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

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.

Suggested Citation

  • Fister, Iztok & Iglesias, Andres & Galvez, Akemi & Del Ser, Javier & Osaba, Eneko & Fister, Iztok & Perc, Matjaž & Slavinec, Mitja, 2019. "Novelty search for global optimization," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 865-881.
    • Handle: RePEc:eee:apmaco:v:347:y:2019:i:c:p:865-881
    DOI: 10.1016/j.amc.2018.11.052
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    References listed on IDEAS

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    1. Iztok Fister & Marjan Mernik & Bogdan Filipič, 2013. "Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm," Computational Optimization and Applications, Springer, vol. 54(3), pages 741-770, April.
    2. Agoston E. Eiben & Jim Smith, 2015. "From evolutionary computation to the evolution of things," Nature, Nature, vol. 521(7553), pages 476-482, May.
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    1. Yu, Caiyang & Cai, Zhennao & Ye, Xiaojia & Wang, Mingjing & Zhao, Xuehua & Liang, Guoxi & Chen, Huiling & Li, Chengye, 2020. "Quantum-like mutation-induced dragonfly-inspired optimization approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 259-289.
    2. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    3. Kutlu Onay, Funda & Aydemı̇r, Salih Berkan, 2022. "Chaotic hunger games search optimization algorithm for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 514-536.
    4. Yan, Zheping & Yan, Jinyu & Wu, Yifan & Cai, Sijia & Wang, Hongxing, 2023. "A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 55-86.

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