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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dengiz, O. Dozier, G. Smith, A.E. |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Ind. & Syst. Eng., Auburn Univ., AL, USA (Dengiz, O.) |
| Abstract | A non-deterministic decoding algorithm for binary coded genetic algorithms is presented. The proposed algorithm enhances the precision of the GA solutions by introducing a Gaussian perturbation to the decoding function. This non-deterministic decoding enables individuals to represent any point in the continuum instead of finite discrete points. As the generations evolve, information gathered from the most fit members is continuously used to rearrange the binary representation grid on the search space, thus establishing a search memory such that the best known individual is always positioned at the center of the Gaussian offset. |
| Sponsorship | IEEE Neural Network Soc. Evolutionary Programming Soc. IEE |
| Starting Page | 2166 |
| Ending Page | 2172 |
| File Size | 344276 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780385152 |
| DOI | 10.1109/CEC.2004.1331165 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-06-19 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Decoding Genetic algorithms Genetic engineering Systems engineering and theory Biological cells Computer industry Evolutionary computation Computer science Mesh generation Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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