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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/1652/1/11_M_M_Rahman_28072010_9_clean.pdf
http://umpir.ump.edu.my/id/eprint/1652/
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spelling my.ump.umpir.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)
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/1652/1/11_M_M_Rahman_28072010_9_clean.pdf
http://umpir.ump.edu.my/id/eprint/1652/
_version_ 1643664439711367168
score 13.211869

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