| dc.contributor | 資管系 | |
| dc.creator (作者) | Hong, C.-F.;Yang, Hsiao-Fang;Wang, L.-H.;Lin, M.-H.;Yang, P.-W.;Lin, G.-S. | |
| dc.creator (作者) | 楊筱芳 | zh_TW |
| dc.date (日期) | 2006 | |
| dc.date.accessioned | 21-Jul-2015 15:05:58 (UTC+8) | - |
| dc.date.available | 21-Jul-2015 15:05:58 (UTC+8) | - |
| dc.date.issued (上傳時間) | 21-Jul-2015 15:05:58 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/76743 | - |
| dc.description.abstract (摘要) | In this paper, our model supplies designing environment that used the component network to identify the high score components and weak components which decrease the number of components to build a meaningful and easily analysis simple graph. Secondary analysis is the bipartite network as the method for formatting the structure or the structure knowledge. In this step the different clusters` components could link each other, but the linkage could not connect the components on same cluster. Furthermore, some weak ties` components or weak links are emerged by Bipartite Graph based Interactive Genetic Algorithm (BiGIGA) to assemble the creative products for customers. Finally, we investigated two significantly different cases. Case one, the customer did not change his preference, and the Wilcoxon test was used to evaluate the difference between IGA and BiGIGA. The results indicated that our model could correctly and directly capture the customer wanted. Case two, after the Wilcoxon test, it evidenced the lateral transmitting using triad closure extent the conceptual network, which could increase the weight of weak relation and retrieved a good product for the customer. The lateral transmitting did not present its convergent power on evolutionary design, but the lateral transmitting has illustrated that it could quickly discover the customer`s favorite value and recombined the creative product. © Springer-Verlag Berlin Heidelberg 2006. | |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 3907 LNCS, Pages 554-564 | |
| dc.subject (關鍵詞) | Computation theory; Computer networks; Evolutionary algorithms; Genetic algorithms; Graphic methods; BiGIGA; Bipartite; Chance discovery; IEC; Interactive computer graphics | |
| dc.title (題名) | Creating chance by new interactive evolutionary computation: Bipartite graph based interactive genetic algorithm | |
| dc.type (資料類型) | conference | en |
