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Conference Paper: A genetic algorithm for the minimum weight triangulation
| Title | A genetic algorithm for the minimum weight triangulation |
|---|---|
| Authors | |
| Keywords | genetic algorithm adaptive genetic operators crossover mutation the minimum weight triangulation |
| Issue Date | 1997 |
| Publisher | IEEE. |
| Citation | IEEE International Conference on Evolutionary Computation Proceedings, Indianapolis, IN, 13-16 April 1997, p. 541-546 How to Cite? |
| Abstract | In this paper, a new method for the minimum weight triangulation of points on a plane, called genetic minimum weight triangulation (GMWT), is presented based on the rationale of genetic algorithms. Polygon crossover and its algorithm for triangulations are proposed. New adaptive genetic operators, or adaptive crossover and mutation operators, are introduced. It is shown that the new method for the minimum weight triangulation can obtain more optimal results of triangulations than the greedy algorithm. |
| Persistent Identifier | http://hdl.handle.net/10722/45578 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Qin, KH | en_HK |
| dc.contributor.author | Wang, WP | en_HK |
| dc.contributor.author | Gong, ML | en_HK |
| dc.date.accessioned | 2007-10-30T06:29:35Z | - |
| dc.date.available | 2007-10-30T06:29:35Z | - |
| dc.date.issued | 1997 | en_HK |
| dc.identifier.citation | IEEE International Conference on Evolutionary Computation Proceedings, Indianapolis, IN, 13-16 April 1997, p. 541-546 | en_HK |
| dc.identifier.uri | http://hdl.handle.net/10722/45578 | - |
| dc.description.abstract | In this paper, a new method for the minimum weight triangulation of points on a plane, called genetic minimum weight triangulation (GMWT), is presented based on the rationale of genetic algorithms. Polygon crossover and its algorithm for triangulations are proposed. New adaptive genetic operators, or adaptive crossover and mutation operators, are introduced. It is shown that the new method for the minimum weight triangulation can obtain more optimal results of triangulations than the greedy algorithm. | en_HK |
| dc.format.extent | 531633 bytes | - |
| dc.format.extent | 3373 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | text/plain | - |
| dc.language | eng | en_HK |
| dc.publisher | IEEE. | en_HK |
| dc.relation.ispartof | IEEE International Conference on Evolutionary Computation Proceedings | - |
| dc.rights | ©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
| dc.subject | genetic algorithm | en_HK |
| dc.subject | adaptive genetic operators | en_HK |
| dc.subject | crossover | en_HK |
| dc.subject | mutation | en_HK |
| dc.subject | the minimum weight triangulation | en_HK |
| dc.title | A genetic algorithm for the minimum weight triangulation | en_HK |
| dc.type | Conference_Paper | en_HK |
| dc.description.nature | published_or_final_version | en_HK |
| dc.identifier.doi | 10.1109/ICEC.1997.592370 | en_HK |
| dc.identifier.hkuros | 27269 | - |