NDLI: Function optimization using a pipelined genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Pakhira, M.K. De, R.K.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Kalyani Gov. Eng. Coll., India (Pakhira, M.K.)
Abstract	Genetic algorithms (GAs) are very commonly used as function optimizers, basically due to their search capability. A number of different serial and parallel versions of GA exist. A pipelined version of a commonly used genetic algorithm is described. The main idea of achieving pipelined execution of different operations of GA is to use a stochastic selection function which works with the fitness value of the candidate chromosome only. The GA with this selection operator is termed PLGA. When executed in a CGA (classical genetic algorithm) framework, the stochastic selection scheme gives performances comparable with the roulette-wheel selection. In the pipelined hardware environment, PLGA is much faster than the CGA. When executed on similar hardware platforms, PLGA may attain a maximum speedup of four over CGA. However, if CGA is executed in a uniprocessor system, the speedup is much more. A comparison of PLGA against PGA (parallel genetic algorithms) is also done.
Starting Page	253
Ending Page	257
File Size	557908
Page Count	5
File Format	PDF
ISBN	0780388941
DOI	10.1109/ISSNIP.2004.1417471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-14
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Genetic mutations Government Stochastic processes Genetic engineering Hardware Biological cells Genetic algorithms Electronics packaging Pipeline processing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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