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A Dynamic Programming based GA for 0-1 Modified Knapsack Problem

by Zaheed Ahmed, Irfan Younas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 16 - Number 7
Year of Publication: 2011
Authors: Zaheed Ahmed, Irfan Younas
10.5120/2028-2668

Zaheed Ahmed, Irfan Younas . A Dynamic Programming based GA for 0-1 Modified Knapsack Problem. International Journal of Computer Applications. 16, 7 ( February 2011), 1-6. DOI=10.5120/2028-2668

@article{ 10.5120/2028-2668,
author = { Zaheed Ahmed, Irfan Younas },
title = { A Dynamic Programming based GA for 0-1 Modified Knapsack Problem },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 16 },
number = { 7 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume16/number7/2028-2668/ },
doi = { 10.5120/2028-2668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:13.926459+05:30
%A Zaheed Ahmed
%A Irfan Younas
%T A Dynamic Programming based GA for 0-1 Modified Knapsack Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 16
%N 7
%P 1-6
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The classical 0-1 knapsack problem is one of the more studied combinatorial optimization problem which belong to the NP class of algorithms. A number of its generalized forms have been addressed by various researchers using different designing techniques. In this paper, we design and analyze the Multiple Knapsack Problems (MKP) by using genetic algorithms. A modified Genetic Algorithm (mGA) is developed with the key focus on efficient encoding scheme for binary string representation and a competent dynamic programming based method for initial population generation. Furthermore transposition is applied in mGA instead of crossover for maintaining the population diversity. Performance analysis of the mGA, justifies our claims that the population incorporates adequate quality and diversity to reach a near optimal solution and transposition reduces the overall computation time.

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Index Terms

Computer Science
Information Sciences

Keywords

Multiple knapsack problem Genetic algorithm Dynamic programming and transposition

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