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...

Full description

Bibliographic Details
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/ http://ir.uitm.edu.my/id/eprint/11938/1/TM_AHMAD%20FIRDAUS%20BIN%20AHMAD%20FADZIL%20CS%2014_5%201.pdf

id	uitm-11938
recordtype	eprints
spelling	uitm-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.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
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/ http://ir.uitm.edu.my/id/eprint/11938/1/TM_AHMAD%20FIRDAUS%20BIN%20AHMAD%20FADZIL%20CS%2014_5%201.pdf
first_indexed	2023-09-18T22:48:46Z
last_indexed	2023-09-18T22:48:46Z
_version_	1777417402447822848

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

Similar Items