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Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil

Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution tim...

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Main Author: Ahmad, Ahmad Firdaus
Format: Thesis
Language:English
Published: 2014
Subjects:
Electronic Computers. Computer Science
Online Access:http://ir.uitm.edu.my/id/eprint/11938/1/TM_AHMAD%20FIRDAUS%20BIN%20AHMAD%20FADZIL%20CS%2014_5%201.pdf
http://ir.uitm.edu.my/id/eprint/11938/
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spelling my.uitm.ir.119382016-05-14T07:20:08Z http://ir.uitm.edu.my/id/eprint/11938/ Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil Ahmad, Ahmad Firdaus Electronic Computers. Computer Science Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of "map and reduce" The Performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. The Performance comparison between GA via MR and PSO via MR also shows that both algorithms are comparable in terms of speedup and efficiency 2014 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/11938/1/TM_AHMAD%20FIRDAUS%20BIN%20AHMAD%20FADZIL%20CS%2014_5%201.pdf Ahmad, Ahmad Firdaus (2014) Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil. Masters thesis, Universiti Teknologi MARA.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Electronic Computers. Computer Science
spellingShingle Electronic Computers. Computer Science
Ahmad, Ahmad Firdaus
Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
description Evolutionary computation (EC) is a method that is ubiquitously used to solve complex computation. Examples of EC such as Genetic Algorithm (GA) and PSO (Particle Swarm Optimization) are prevalent due to their efficiency and effectiveness. Despite these advantages, EC suffers from long execution time due to its parallel nature. Therefore, this research explores the prospect of speeding up the EC algorithms specifically GA and PSO via MapReduce (MR) parallel processing framework. MR is an emerging parallel processing framework that hides the complex parallelization processes by employing the functional abstraction of "map and reduce" The Performance of the parallelized GA via MR and PSO via MR are evaluated using an analogous case study to find out the speedup and efficiency in order to measure the scalability of both proposed algorithms. Comparisons between GA via MR and PSO via MR are also established in order to find which EC algorithm scales better via MR parallel processing framework. From the results and analysis obtained from this research, it is established that both GA and PSO can be efficiently parallelized and shows good scalability via MR parallel processing framework. The Performance comparison between GA via MR and PSO via MR also shows that both algorithms are comparable in terms of speedup and efficiency
format Thesis
author Ahmad, Ahmad Firdaus
author_facet Ahmad, Ahmad Firdaus
author_sort Ahmad, Ahmad Firdaus
title Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
title_short Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
title_full Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
title_fullStr Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
title_full_unstemmed Analysis of evolutionary computing performance via mapreduce parallel processing architecture / Ahmad Firdaus Ahmad Fadzil
title_sort analysis of evolutionary computing performance via mapreduce parallel processing architecture / ahmad firdaus ahmad fadzil
publishDate 2014
url http://ir.uitm.edu.my/id/eprint/11938/1/TM_AHMAD%20FIRDAUS%20BIN%20AHMAD%20FADZIL%20CS%2014_5%201.pdf
http://ir.uitm.edu.my/id/eprint/11938/
_version_ 1685648334760443904
score 13.211869

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