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Adaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems

by Jyoti Chaturvedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 4
Year of Publication: 2014
Authors: Jyoti Chaturvedi
10.5120/18743-9996

Jyoti Chaturvedi . Adaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems. International Journal of Computer Applications. 107, 4 ( December 2014), 34-42. DOI=10.5120/18743-9996

@article{ 10.5120/18743-9996,
author = { Jyoti Chaturvedi },
title = { Adaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 4 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number4/18743-9996/ },
doi = { 10.5120/18743-9996 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:13.750633+05:30
%A Jyoti Chaturvedi
%T Adaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 4
%P 34-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The development in the field of quantum computing gives us a significant edge over classical computing in terms of time and efficiency. This is particularly useful for NP-hard problems such as graph layout problems. Since many real world problems are effectively solved by genetic algorithm (GA) and the performance of GA highly depends upon the setting of its parameters, therefore this paper focuses on a Quantum Inspired Genetic Algorithm (QIGA) and develops and evaluates adaptive strategies for the same. QIGA adapts ideas of Q-bits, superposition of Q-bits from quantum computing. The effectiveness and the applicability of adaptive QIGA is demonstrated by experimental results on the benchmark Knapsack, Maxcut and Onemax combinatorial optimization problems. The results show that adaptive QIGA is superior to QIGAs.

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Index Terms

Computer Science
Information Sciences

Keywords

Quantum inspired genetic algorithm Parameter control adaptive QIGA.

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