An enhancement of selection and crossover operations in real-coded genetic algorithm for large-dimensionality optimization

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• 作者：Noh Sung Kwak ; Jongsoo Lee
• 关键词：Real ; coded genetic algorithm ; Large ; scale global optimization ; Subpopulation ; Probabilistic crossover
• 刊名：Journal of Mechanical Science and Technology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：30
• 期：1
• 页码：237-247
• 全文大小：750 KB
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• 作者单位：Noh Sung Kwak (1)
  Jongsoo Lee (1)
  
  1. School of Mechanical Engineering, Yonsei University, Seoul, 120-749, Korea
  
• 刊物类别：Engineering
• 刊物主题：Mechanical Engineering
  Structural Mechanics
  Control Engineering
  Industrial and Production Engineering
  
• 出版者：The Korean Society of Mechanical Engineers
• ISSN：1976-3824

文摘

The present study aims to implement a new selection method and a novel crossover operation in a real-coded genetic algorithm. The proposed selection method facilitates the establishment of a successively evolved population by combining several subpopulations: an elitist subpopulation, an off-spring subpopulation and a mutated subpopulation. A probabilistic crossover is performed based on the measure of probabilistic distance between the individuals. The concept of ‘allowance’ is suggested to describe the level of variance in the crossover operation. A number of nonlinear/non-convex functions and engineering optimization problems are explored to verify the capacities of the proposed strategies. The results are compared with those obtained from other genetic and nature-inspired algorithms.