Application Of Genetic Algorithms For Robust Parameter Optimization

Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own speci...

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Main Author:	Belavendram, N.
Format:	Article
Language:	English
Published:	Universiti Malaysia Pahang 2010
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1652/ http://umpir.ump.edu.my/id/eprint/1652/1/11_M_M_Rahman_28072010_9_clean.pdf

id	ump-1652
recordtype	eprints
spelling	ump-16522015-03-03T07:52:10Z http://umpir.ump.edu.my/id/eprint/1652/ Application Of Genetic Algorithms For Robust Parameter Optimization Belavendram, N. TJ Mechanical engineering and machinery Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution. Universiti Malaysia Pahang 2010 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1652/1/11_M_M_Rahman_28072010_9_clean.pdf Belavendram, N. (2010) Application Of Genetic Algorithms For Robust Parameter Optimization. International Journal of Automotive and Mechanical Engineering (IJAME), 2. pp. 211-220. ISSN 1985-9325(Print); ISSN: 2180-1606 (Online)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Belavendram, N. Application Of Genetic Algorithms For Robust Parameter Optimization
description	Parameter optimization can be achieved by many methods such as Monte-Carlo, full, and fractional factorial designs. Genetic algorithms (GA) are fairly recent in this respect but afford a novel method of parameter optimization. In GA, there is an initial pool of individuals each with its own specific phenotypic trait expressed as a ‘genetic chromosome’. Different genes enable individuals with different fitness levels to reproduce according to natural reproductive gene theory. This reproduction is established in terms of selection, crossover and mutation of reproducing genes. The resulting child generation of individuals has a better fitness level akin to natural selection, namely evolution. Populations evolve towards the fittest individuals. Such a mechanism has a parallel application in parameter optimization. Factors in a parameter design can be expressed as a genetic analogue in a pool of sub-optimal random solutions. Allowing this pool of sub-optimal solutions to evolve over several generations produces fitter generations converging to a pre-defined engineering optimum. In this paper, a genetic algorithm is used to study a seven factor non-linear equation for a Wheatstone bridge as the equation to be optimized. A comparison of the full factorial design against a GA method shows that the GA method is about 1200 times faster in finding a comparable solution.
format	Article
author	Belavendram, N.
author_facet	Belavendram, N.
author_sort	Belavendram, N.
title	Application Of Genetic Algorithms For Robust Parameter Optimization
title_short	Application Of Genetic Algorithms For Robust Parameter Optimization
title_full	Application Of Genetic Algorithms For Robust Parameter Optimization
title_fullStr	Application Of Genetic Algorithms For Robust Parameter Optimization
title_full_unstemmed	Application Of Genetic Algorithms For Robust Parameter Optimization
title_sort	application of genetic algorithms for robust parameter optimization
publisher	Universiti Malaysia Pahang
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1652/ http://umpir.ump.edu.my/id/eprint/1652/1/11_M_M_Rahman_28072010_9_clean.pdf
first_indexed	2023-09-18T21:54:51Z
last_indexed	2023-09-18T21:54:51Z
_version_	1777414010181779456

Application Of Genetic Algorithms For Robust Parameter Optimization

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