poster

A Homologous Gene Replacement based Genetic Algorithm

Authors:

Md Tamjidul HoqueAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 91 - 92

https://doi.org/10.1145/2908961.2909020

Published: 20 July 2016 Publication History

Abstract

This paper introduces a new genetic operator, called homologous gene replacement (hGR) applied to the chromosome of genetic algorithm (GA). The new genetic algorithm is referred as hGRGA. This operator aims to extend the ground idea behind the biological evolutionary process based classical genetic algorithm that relies on localizing and utilizing good local schema present in the genes of a chromosome. The operator furbishes the chromosomes in gene level to boost their overall functionality. The proposed hGRGA is evaluated by widely-used benchmark functions. The simulation results was promising in terms of convergence speed and preciseness in finding optima.

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Cited By

Rastgoufard SIqbal SHoque MCharalampidis D(2018)Genetic algorithm variant based effective solutions for economic dispatch problems2018 IEEE Texas Power and Energy Conference (TPEC)10.1109/TPEC.2018.8312096(1-6)Online publication date: Feb-2018
https://doi.org/10.1109/TPEC.2018.8312096

Index Terms

A Homologous Gene Replacement based Genetic Algorithm
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

cover image ACM Conferences

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Poster

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Louisiana Board of Regents

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Rastgoufard SIqbal SHoque MCharalampidis D(2018)Genetic algorithm variant based effective solutions for economic dispatch problems2018 IEEE Texas Power and Energy Conference (TPEC)10.1109/TPEC.2018.8312096(1-6)Online publication date: Feb-2018
https://doi.org/10.1109/TPEC.2018.8312096

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