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

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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.

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Authors and Affiliations

  1. School of Mechanical Engineering, Yonsei University, Seoul, 120-749, Korea

    Noh Sung Kwak & Jongsoo Lee

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  1. Noh Sung Kwak
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  2. Jongsoo Lee
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Correspondence to Jongsoo Lee.

Additional information

Jongsoo Lee received B.S. in Mechanical Engineering at Yonsei University, Korea in 198 and Ph.D. in Mechanical Engineering at Rensselaer Polytechnic Institute, Troy, NY in 1996. After a research associate at Rensselaer Rotorcraft Technology Center, he is a professor of Mechanical Engineering at Yonsei University. His research interests include multidisciplinary/multi-physics/multi-scale design optimization and reliability-based robust engineering design with applications to structures, structural dynamics, fluid-structure interactions and flow induced noise and vibration problems.

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Kwak, N.S., Lee, J. An enhancement of selection and crossover operations in real-coded genetic algorithm for large-dimensionality optimization. J Mech Sci Technol 30, 237–247 (2016). https://doi.org/10.1007/s12206-015-1227-2

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  • DOI: https://doi.org/10.1007/s12206-015-1227-2

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