Modifiable Boltzmann selective pressure is investigated as a tool to control variability in optimizations using genetic algorithms. An implementation of variable selective pressure, modeled after the use of temperature as a parameter in simulated annealing approaches, is described. The convergence behavior of optimization runs is illustrated as a function of selective pressure; the method is compared to a genetic algorithm lacking this control feature and is shown to exhibit superior convergence properties on a small set of test problems. An analysis is presented that compares the selective pressure of this algorithm to a standard selection procedure.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5967 |
| Date | 01 December 1991 |
| Creators | de la Maza, Michael, Tidor, Bruce |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 19 p., 1678653 bytes, 1307750 bytes, application/postscript, application/pdf |
| Relation | AIM-1345 |
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