NDLI: Simple genetic algorithm parameter selection for protein structure prediction

Content Provider	IEEE Xplore Digital Library
Author	Gates Jr., G.H. Merkle, L.D. Lamont, G.B. Pachter, R.
Copyright Year	1995
Description	Author affiliation: Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA (Gates, G.H., Jr.; Merkle, L.D.; Lamont, G.B.)
Abstract	Selection of run-time parameters is a critical step in the application of genetic algorithms (GAs). Numerous investigations have discussed parameter set selection, both theoretically and empirically. Theoretical work has focused on the choice of population size, while empirical studies cover a wide range of GA parameters. Theory suggests population sizes which increase exponentially with string length. The available experimental data suggests small populations perform consistently well, but the test problems are limited to small string lengths. Thus, we still do not have a complete understanding of how parameters should be chosen, especially for problems with large string lengths. This study extends Schaffer's (1989) results by performing a similar empirical analysis of GA parameters on a real-world application (protein structure prediction), with longer string lengths and a very large number of local optima. Relationships between population size, mutation rates and crossover rates similar to those reported by Schaffer are shown.
Starting Page	620
Ending Page	624
File Size	437547
Page Count	5
File Format	PDF
ISBN	0780327594
DOI	10.1109/ICEC.1995.487455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-29
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Testing Genetic mutations Runtime Performance analysis Guidelines Protein engineering Performance evaluation
Content Type	Text
Resource Type	Article

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