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| 題名 | A Knowledge-Evolution Strategy Based on Genetic Programming |
| 作者 | Chen, Chuen-lung;Kuo, Chan-Sheng;Hong, Tzung-Pei 陳春龍 |
| 貢獻者 | 資管系 |
| 日期 | 2008 |
| 上傳時間 | 17-六月-2015 17:20:19 (UTC+8) |
| 摘要 | Knowledge evolution is an important issue in knowledge management since enterprises face keen competition and need to keep the latest knowledge with time in an organization. In this paper, we proposed a GP-based knowledge-evolution framework to search for a good integrated classification tree with different evolving time points. The proposed approach can learn the evolving knowledge, integrating original and new knowledge, to deal properly with the organizational need for updating the latest knowledge as time goes on in a dynamic environment. In addition, we developed the initial population, consisting of four proportions, to accomplish suitable diversity and thus raise the search range as well as next learning efficiency in the evolutionary process. |
| 關聯 | International Conference on Hybrid Information Technology - ICHIT |
| 資料類型 | conference |
| DOI | http://dx.doi.org/10.1109/ICHIT.2008.169 |
| dc.contributor | 資管系 | |
| dc.creator (作者) | Chen, Chuen-lung;Kuo, Chan-Sheng;Hong, Tzung-Pei | |
| dc.creator (作者) | 陳春龍 | zh_TW |
| dc.date (日期) | 2008 | |
| dc.date.accessioned | 17-六月-2015 17:20:19 (UTC+8) | - |
| dc.date.available | 17-六月-2015 17:20:19 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-六月-2015 17:20:19 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75932 | - |
| dc.description.abstract (摘要) | Knowledge evolution is an important issue in knowledge management since enterprises face keen competition and need to keep the latest knowledge with time in an organization. In this paper, we proposed a GP-based knowledge-evolution framework to search for a good integrated classification tree with different evolving time points. The proposed approach can learn the evolving knowledge, integrating original and new knowledge, to deal properly with the organizational need for updating the latest knowledge as time goes on in a dynamic environment. In addition, we developed the initial population, consisting of four proportions, to accomplish suitable diversity and thus raise the search range as well as next learning efficiency in the evolutionary process. | |
| dc.format.extent | 130 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | International Conference on Hybrid Information Technology - ICHIT | |
| dc.title (題名) | A Knowledge-Evolution Strategy Based on Genetic Programming | |
| dc.type (資料類型) | conference | en |
| dc.identifier.doi (DOI) | 10.1109/ICHIT.2008.169 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1109/ICHIT.2008.169 |