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Title: A novel crossover operator designed to exploit synergies of two crossover operators for real-coded genetic algorithms
Authors: Shashi
Deep, K.
Deep K.
Das K.N.
Bansal J.C.
Pant M.
Nagar A.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. The performance of DDX is compared with existing real-coded crossover operator namely Laplace crossover (LX). DDX is used in conjunction with a well-known mutation operator; Power mutation (PM) to obtain a new generational real-coded genetic algorithm called DDX-PM. DDX-PM is compared with the existing LX-PM. The performance of both the genetic algorithms is compared on the basis of success rate, average function evaluation, average error and computational time, and the preeminence of the proposed crossover operator is established. © Springer Science+Business Media Singapore 2016.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2016), 343- 350
Issue Date: 2016
Publisher: Springer Verlag
Keywords: Crossover operator
Mutation operator
Real-coded genetic algorithm
Genetic algorithms
Soft computing
Average errors
Computational time
Crossover operator
Double distribution
Mutation operators
Real coded genetic algorithm
Problem solving
ISBN: 9.79E+12
ISSN: 21945357
Author Scopus IDs: 8301540900
Author Affiliations: Shashi, Control and Decision Systems Laboratory, Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India
Corresponding Author: Shashi; Control and Decision Systems Laboratory, Department of Aerospace Engineering, Indian Institute of ScienceIndia; email:
Appears in Collections:Conference Publications [MA]

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