Geometric probabilistic evolutionary algorithm
作者：
Highlights：
• Probabilistic analysis of geometric transformations leads to new search operators.
• The nonlinearity of inversions w.r.t. hyperspheres is imitated stochastically.
• Probabilistic search mechanisms inherit geometric transformation properties.
• Uniformly distributed reflections add exploration capabilities to an EA.
• Scientific contributions show competitive performance in benchmark problems.
摘要
•Probabilistic analysis of geometric transformations leads to new search operators.•The nonlinearity of inversions w.r.t. hyperspheres is imitated stochastically.•Probabilistic search mechanisms inherit geometric transformation properties.•Uniformly distributed reflections add exploration capabilities to an EA.•Scientific contributions show competitive performance in benchmark problems.
论文关键词：Real parameter optimisation,Spherical inversion,Evolutionary algorithm,Multivariate distribution
论文评审过程：Received 1 August 2019, Revised 18 October 2019, Accepted 7 November 2019, Available online 27 November 2019, Version of Record 6 December 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113080
