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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| Format: | Article |
| Language: | English |
| Published: |
Universiti Malaysia Pahang
2010
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| 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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| Summary: | 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. |
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