Global optimization using a genetic algorithm with hierarchically structured population
作者：
Highlights：
• A genetic algorithm (GA) with hierarchically structured population is evaluated.
• The GA is applied to solve benchmark unconstrained optimization problems.
• Computational tests evaluate different structures, population sizes and crossover operators.
• The results found are also compared with those found by other methods from the literature.
• The GA outperforms other approaches for the number of function evaluations and success rate.
摘要
•A genetic algorithm (GA) with hierarchically structured population is evaluated.•The GA is applied to solve benchmark unconstrained optimization problems.•Computational tests evaluate different structures, population sizes and crossover operators.•The results found are also compared with those found by other methods from the literature.•The GA outperforms other approaches for the number of function evaluations and success rate.
论文关键词：Genetic algorithms,Global optimization,Continuous optimization,Population set-based methods,Hierarchical structure
论文评审过程：Received 11 May 2012, Revised 27 June 2013, Available online 20 November 2013.
论文官网地址：https://doi.org/10.1016/j.cam.2013.11.008
