Fuzzy-Classification Assisted Solution Preselection in Evolutionary Optimization


  • Aimin Zhou East China Normal University
  • Jinyuan Zhang East China Normal University
  • Jianyong Sun Xi'an Jiaotong University
  • Guixu Zhang East China Normal University




In evolutionary optimization, the preselection is an efficient operator to improve the search efficiency, which aims to filter unpromising candidate solutions before fitness evaluation. Most existing preselection operators rely on fitness values, surrogate models, or classification models. Basically, the classification based preselection regards the preselection as a classification procedure, i.e., differentiating promising and unpromising candidate solutions. However, the difference between promising and unpromising classes becomes fuzzy as the running process goes on, as all the left solutions are likely to be promising ones. Facing this challenge, this paper proposes a fuzzy classification based preselection (FCPS) scheme, which utilizes the membership function to measure the quality of candidate solutions. The proposed FCPS scheme is applied to two state-of-the-art evolutionary algorithms on a test suite. The experimental results show the potential of FCPS on improving algorithm performance.




How to Cite

Zhou, A., Zhang, J., Sun, J., & Zhang, G. (2019). Fuzzy-Classification Assisted Solution Preselection in Evolutionary Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2403-2410. https://doi.org/10.1609/aaai.v33i01.33012403



AAAI Technical Track: Heuristic Search and Optimization