Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm

Abid Hussain; Yousaf Shad Muhammad; Muhammad Nauman Sajid

doi:doi:10.11648/j.mcs.20170206.12

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Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm

Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid

Published in Mathematics and Computer Science (Volume 2, Issue 6)

Received: 27 September 2017 Accepted: 19 October 2017 Published: 20 November 2017

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Abstract

Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.

Published in	Mathematics and Computer Science (Volume 2, Issue 6)
DOI	10.11648/j.mcs.20170206.12
Page(s)	89-97
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Genetic Algorithm, Genetic Operators, Selection Criterion, Benchmark Problems

References

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[4]	Zbigniew Michalewicz. Genetic Algorithms+ Data Structures= Evolution Programs. Springer, 3rd edition, 1996.
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[6]	Thomas Back and Frank Hoffmeister. Extended selection mechanisms in genetic algorithms. Citeseer, pages 92–99, 1991.
[7]	David E Goldberg and Kalyanmoy Deb. A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms, 1:69–93, 1991.
[8]	Tobias Blickle and Lothar Thiele. A comparison of selection schemes used in genetic algorithms. TIK-Report 11, TIK Institut fur Technische und Kommunikationsnetze, Swiss Federal Institute of Technology, 1995.
[9]	Rakesh Kumar. Blending roulette wheel selection & rank selection in genetic algorithms. International Journal of Machine Learning and Computing, 2 (4): 365, 2012.
[10]	Khalid Jebari and Mohammed Madiafi. Selection methods for genetic algorithms. International Journal of Emerging Sciences, 3 (4): 333–344, 2013.
[11]	Smit Anand, Nishat Afreen, and Shama Yazdani. A novel and efficient selection method in genetic algorithm. International Journal of Computer Applications, 129 (15): 7–12, 2015.
[12]	Hari Mohan Pandey. Performance evaluation of selection methods of genetic algorithm and network security concerns. Procedia Computer Science, 78:13–18, 2016.
[13]	James E Baker. Reducing bias and inefficiency in the selection algorithm. In Proceedings of the second international conference on genetic algorithms, pages 14–21, 1987.
[14]	Anne Brindle. Genetic algorithms for function optimization. Ph. D. Thesis, University of Alberta, Edmonton, 1981.
[15]	James Edward Baker. Adaptive selection methods for genetic algorithms. In Proceedings of an International Conference on Genetic Algorithms and their applications, pages 101–111. Hillsdale, New Jersey, 1985.
[16]	Ingo Rechenberg. Cybernetic solution path of an experimental problem. Ministry of Aviation, Royal Aircraft Establishment, 1965.
[17]	Kalyanmoy Deb and David E Goldberg. An investigation of niche and species formation in genetic function optimization. In Proceedings of the 3rd international conference on genetic.
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[19]	Heinz Mu hlenbein. Parallel genetic algorithms, population genetics and combinatorial optimization. In Workshop on Parallel Processing: Logic, Organization, and Technology, pages 398–406. Springer, 1989.
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[21]	Thomas Back. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press, 1996.
[22]	Kenneth DeJong. An analysis of the behavior of a class of genetic adaptive systems. Ph. D. Thesis, University of Michigan, 1975.
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[24]	Eric E Easom. A survey of global optimization techniques. PhD thesis, University of Louisville, 1990.

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Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid. (2017). Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Mathematics and Computer Science, 2(6), 89-97. https://doi.org/10.11648/j.mcs.20170206.12

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ACS Style

Abid Hussain; Yousaf Shad Muhammad; Muhammad Nauman Sajid. Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Math. Comput. Sci. 2017, 2(6), 89-97. doi: 10.11648/j.mcs.20170206.12

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AMA Style

Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid. Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Math Comput Sci. 2017;2(6):89-97. doi: 10.11648/j.mcs.20170206.12

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@article{10.11648/j.mcs.20170206.12,
  author = {Abid Hussain and Yousaf Shad Muhammad and Muhammad Nauman Sajid},
  title = {Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm},
  journal = {Mathematics and Computer Science},
  volume = {2},
  number = {6},
  pages = {89-97},
  doi = {10.11648/j.mcs.20170206.12},
  url = {https://doi.org/10.11648/j.mcs.20170206.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20170206.12},
  abstract = {Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.},
 year = {2017}
}

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TY  - JOUR
T1  - Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm
AU  - Abid Hussain
AU  - Yousaf Shad Muhammad
AU  - Muhammad Nauman Sajid
Y1  - 2017/11/20
PY  - 2017
N1  - https://doi.org/10.11648/j.mcs.20170206.12
DO  - 10.11648/j.mcs.20170206.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 89
EP  - 97
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20170206.12
AB  - Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.
VL  - 2
IS  - 6
ER  -

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Author Information

Abid Hussain

Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
Yousaf Shad Muhammad

Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
Muhammad Nauman Sajid

Department of Software Engineering, Foundation University, Islamabad, Pakistan

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APA Style

Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid. (2017). Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Mathematics and Computer Science, 2(6), 89-97. https://doi.org/10.11648/j.mcs.20170206.12

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ACS Style

Abid Hussain; Yousaf Shad Muhammad; Muhammad Nauman Sajid. Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Math. Comput. Sci. 2017, 2(6), 89-97. doi: 10.11648/j.mcs.20170206.12

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AMA Style

Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid. Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm. Math Comput Sci. 2017;2(6):89-97. doi: 10.11648/j.mcs.20170206.12

Copy | Download

@article{10.11648/j.mcs.20170206.12,
  author = {Abid Hussain and Yousaf Shad Muhammad and Muhammad Nauman Sajid},
  title = {Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm},
  journal = {Mathematics and Computer Science},
  volume = {2},
  number = {6},
  pages = {89-97},
  doi = {10.11648/j.mcs.20170206.12},
  url = {https://doi.org/10.11648/j.mcs.20170206.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20170206.12},
  abstract = {Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm
AU  - Abid Hussain
AU  - Yousaf Shad Muhammad
AU  - Muhammad Nauman Sajid
Y1  - 2017/11/20
PY  - 2017
N1  - https://doi.org/10.11648/j.mcs.20170206.12
DO  - 10.11648/j.mcs.20170206.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 89
EP  - 97
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20170206.12
AB  - Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.
VL  - 2
IS  - 6
ER  -

Copy | Download