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 journalArticlepeer-review

7 Citations (Scopus)

Abstract

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
Volume30
Issue number1
DOIs
Publication statusPublished - 2016 Jan 1

Bibliographical note

Funding Information:
This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2014055282).

Publisher Copyright:
© 2016, The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg.

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering

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