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

Noh Sung Kwak, Jongsoo Lee

Research output: Contribution to journalArticle

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)237-247
Number of pages11
JournalJournal of Mechanical Science and Technology
Issue number1
Publication statusPublished - 2016 Jan 1


All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering

Cite this