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MoHPBGA: Multiobjective Hierarchical Population Balanced Genetic Algorithm using MapReduce

by Poka Laxmi, Jayant Umale, Sunita Mahajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 2
Year of Publication: 2012
Authors: Poka Laxmi, Jayant Umale, Sunita Mahajan
10.5120/4925-7152

Poka Laxmi, Jayant Umale, Sunita Mahajan . MoHPBGA: Multiobjective Hierarchical Population Balanced Genetic Algorithm using MapReduce. International Journal of Computer Applications. 40, 2 ( February 2012), 1-7. DOI=10.5120/4925-7152

@article{ 10.5120/4925-7152,
author = { Poka Laxmi, Jayant Umale, Sunita Mahajan },
title = { MoHPBGA: Multiobjective Hierarchical Population Balanced Genetic Algorithm using MapReduce },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 2 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number2/4925-7152/ },
doi = { 10.5120/4925-7152 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:59.465757+05:30
%A Poka Laxmi
%A Jayant Umale
%A Sunita Mahajan
%T MoHPBGA: Multiobjective Hierarchical Population Balanced Genetic Algorithm using MapReduce
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 2
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Use of heuristic methods is common to find the solutions to the optimization problems for scientific and real time. Problems such as Travelling Salesman (TSP) require more accurate solution which is tried by various optimization methods. Research in this direction shows the use of Genetic algorithms (GA) as promising candidate and is preferred over other optimization methods. Firstly due to the use of large population and secondly large number of iterations GA tends to be more accurate but inefficient with respect to computation time. Variants of GA are formulated and experimented so as to take care of execution time. We present the review of approaches used to formulation of GA solutions mainly parallel GA (PGA), distributed GA (DGA) and hierarchical parallel GA (HPGA). Further this paper proposes Multi objective Hierarchical Population Balanced Genetic Algorithm (MoHPBGA) as the improved candidate which uses map reduce framework for efficient use of population mapping and synchronization of tasks.

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Index Terms

Computer Science
Information Sciences

Keywords

Optimization Genetic Algorithms Parallel Computing Multiobjective problems

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