| Toshiyuki Masui. Evolutionary learning of graph layout constraints from examples. Proceedings of the ACM Symposium on User Interface Software and Technology. ACM Press, pages 103--108, 1994. |
....Threshold # 2 therefore controls the length ratio of segments bound. Identification of a suitable vector # = # 1 ,# 2 ,# 1 ,# 2 ,#,# 1 ,# 2 ) of parameters is a serious problem. Two nested simulated annealing computations are used in [11] to identify parameters of a spring embedder variant. In [9], a genetic algorithm is used to breed a suitable objective function. However, both meth Brandes and Wagner, Layout of Train Graphs , JGAA, 4(3) 135 155 (2000) 144 ods are heuristic in defining their objective as well as in optimizing it. Given one or more examples which are considered to be ....
T. Masui. Evolutionary learning of graph layout constraints from examples. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '94), pages 103--108. ACM, The Association for Computing Machinery, 1994.
....[889] Maeshiro, T. 345] Makino, Hiroshi, 50] Manderick, Bernard, 697] Mares, C. 716] Martin, Juan, 440] Maruyama, T. 1275] Maruyama, Tsutomu, 1393, 1396, 1400, 1588] Maruyama, Y. 1146] Masayuki, Inaba, 567] Masuda, T. 346, 1553] Masui, Shigehiro, 552] Masui, T. [141] Masujima, Y. 151] Masunaga, Shinya, 629, 892] Matayoshi, Naoki, 1519] Matoba, H. 1553] Matsua, H. 553] Matsubara, Y. 397, 645, 1414] Matsuda, Hideo, 1085] Matsuda, H. 1526] Matsuda, K. 1551, 1099] Matsuhisa, Hiroshi, 574] Matsui, K. 47, 142, 554, 893, 1103, 1265] ....
.... [483] GenNETS, 1482] genome variable size, 1365] geometry, 824] geophysics, 615] goal programming nonlinear, 952] GOV, 945, 1013] graph theory minimum spanning tree, 1255] graphics, 1472] graphs, 1588, 109] drawing, 1545] Hamiltonian, 933] independent set, 924] layout, [141] grinding, 1402] gyroscopy, 574] halftoning, 1413, 40, 67, 340, 542] handbook in Japanese, 1579] hardware, 1388, 309] AdAM, 836] design, 843] evalvable, 538] evolvable, 130, 519, 551, 563, 630, 652, 697, 710, 720, 837, 839, 871, 965, 1114] evolving, 623] optimization, ....
[Article contains additional citation context not shown here]
T. Masui. Evolutionary learning of graph layout constraints from examples. In Proceedings of the Seventh Annual Symposium on User Interface Software and Technology, pages 103-108, Marina del Rey, CA, USA, 2.-4. November 1994. ACM, New York, NY. yCCA58146/96 ga94bMasui.
....to lie on different sides of minimal edges (Fig. 4(d) This can even be enforced (Fig. 4(e) The identfication of a suitable set of parameters is a serious problem. Mendon ca and Eades use two nested simulatd annealing computations to identify parameters of a spring embedder variant [ME93] In [Mas94], a genetic algorithm is used to breed a suitable objective function. However, both methods are heuristic in defining their objective as well as in optimizing it. Given one or more examples which are considered to be well done (e.g. by manual rearrangement) a theoretically sound approach would be ....
Toshiyuki Masui. Evolutionary learning of graph layout constraints from examples. Proceedings of the ACM Symposium on User Interface Software and Technology. ACM Press, pages 103--108, 1994.
No context found.
Toshiyuki Masui. Evolutionary learning of graph layout constraints from examples. Proceedings of the ACM Symposium on User Interface Software and Technology. ACM Press, pages 103--108, 1994.
No context found.
T. Masui. Evolutionary learning of graph layout constraints from examples. In Proceedings of the ACM Symposium on User Interface Software and Technology, Demonstrational User Interfaces, pages 103--108, 1994.
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