B. A. Julstrom. Very greedy crossover in a genetic algorithm for the Traveling Salesman Problem. In K. M. George, J. H. Carroll, E. Deaton, D. Oppenheim, and J. Hightower, editors, Proceedings of the 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Houdayer, J. 92, 98] Hsu, Chin Chih, 49, 83] Hsu, Ching Chi, 116] Ikeda, Y. 156] Inoue, K. 84] Inza, I. 93] Isern, G. 91] Jog, P. D. 36] Jog, Prasanna, 128, 154] Johnson, D. S. 113] Johnson, Mika, 95] Johnsson, Mika, 73] Jones, Antonia J. 42, 112] Julstrom, Bryant A. [46, 57, 72] Kadaba, Nagesh, 130] Kakazu, Yukinori, 40, 44] Kanet, John J. 131] Kanzaki, Y. 84] Kao, Cheng Yan, 45, 116] Karp, R. 65] Katayama, K. 85] Kindermann, J. 138] Kita, Hajime, 24] Klimasauskas, Casimir C. 132] Kobayashi, Shigenobu, 161] Kolen, Antoon, 19] Kopfer, Herbert, ....

....Zitzler, Eckart, 97] total 152 articles by 284 di erent authors 16 Genetic algorithms in TSP 4.7 Subject index All subject keywords of the papers given by the editor of this bibliography are shown next. accelerators Linac, 104] analysing ES, 74] analysing GA, 128] ANOVA, 87] crossover, [46] initial population, 75] Markov chains, 101] statistically, 87] TSP, 83] ant colony, 96] ant system, 78] ant systems TSP, 69] arti cial intelligence, 60, 12] ASPARAGOS96, 71] Bayes networks, 60] bin packing, 31] bin packing, 16] 2D, 150] building blocks, 16] CAD, 149] ....

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Bryant A. Julstrom. Very greedy crossover in a genetic algorithm for the traveling salesman problem. In K. M. George, Janice H. Carroll, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Proceedings of the 10th ACM Symposium on Applied Computing, pages 324-328, ?, ? 1995. ACM Press, New York. ga95bJulstrom.

....T. R. 50, 59, 60] Hatcher, W. J. 50, 59, 60] Hekanaho, Jukka, 108, 11, 109, 110] Heuvel, H. M. 22, 28] Hohfeld, Markus, 119] Hohn, Christian, 63, 64, 65, 66, 67] Huber, Reinhold, 94, 95] Hurme, Markku, 118] Hyotyniemi, Heikki, 93] Jaske, Harri, 92] Julstrom, Bryant A. [17, 18, 19, 20, 21] Jumppanen, Anne, 96] Kampen, Antoine H. C. van, 12] Karatza, Helen, 85] Karr, Charles L. 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62] Kateman, Gerrit, 22, 23, 24, 25, 26, 27, 28] Kettunen, Arto, 13, ....

....by the editor of this bibliography are shown next. The keywords neural networks , optimization , and evolution strategies have been omitted in this list because of their high occurrence rate. accelerators particle, 121, 123] aerodynamics, 130] analysing GA, 68, 75, 66] crossover, [18] deception, 102, 104] hardness, 104] long path problems, 67] operators, 20] temporal logic, 136, 137, 138, 139] application, 38, 39] industrial, 119] textile, 30] wood industry, 16] astronomy sunspots, 96, 92, 97] bin packing, 69] CAD, 38, 39, 43, 44, 49, 90] filters, ....

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Bryant A. Julstrom. Very greedy crossover in a genetic algorithm for the traveling salesman problem. In K. M. George, Janice H. Carroll, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Proceedings of the 10th ACM Symposium on Applied Computing, pages 324--328, ?, ? 1995. ACM Press. y(Julstrom) ga95bJulstrom.

....[726] Johnson, Mark S. 375] Johnson, Timothy, 269] Jones, A. 678] Jones, Terry, 651, 290] Jong, Kenneth A. De, 185, 426, 240, 117, 241, 242, 263] Joseph, D. 476] Joyce, G. F. 367] Judson, Richard S. 734, 329, 766, 767] Juell, Paul L. 611] Jukes, Ken, 315] Julstrom, Bryant A. [182, 733, 74] Kadaba, Nagesh, 559] Kahng, Andrew B. 280, 168] Kaiser, C. E. 75] Kajitani, Isamu, 347] Kakazu, Yukinori, 274, 275] Kallel, L. 217] Kalus, A. 466] Kampen, Antoine H. C. van, 202] Kampis, George, 127] Kang, L. 403] Kang, Tae Won, 206] Kao, Cheng Yan, 755] Kappler, Cornelia, ....

....next. acoustics, 82] adaptation, 732] adaptive coding, 133, 134, 135] aerospace rendezvous, 793] alloys, 785] analysing GA, 695, 353, 120, 778, 682, 701, 276, 180, 731, 201, 662, 61] analysing GA ANOVA, 230] coding, 652, 95] continuous space, 723, 157] convergence, 672, 677] crossover, [242, 244, 256, 142, 154, 164, 172, 175, 179, 182, 183, 188, 193, 202, 205, 216, 97] diploidy, 90] diversity, 188, 193, 736, 491] dominance, 60] factor analysis, 772] tness, 316, 317] tness function, 294] tness landscape, 291, 292, 309] tness landscapes, 290] tness moments, 303] forking, 499] in nite population size, 729] information theory, 728] ....

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Bryant A. Julstrom. Very greedy crossover in a genetic algorithm for the traveling salesman problem. In K. M. George, Janice H. Carroll, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Proceedings of the 10th ACM Symposium on Applied Computing, pages 324-328, ?, ? 1995. ACM Press, New York. ga95bJulstrom.

....edges predominate in good and optimal solutions. This suggests that in building candidate solutions, edge selection should favor edges in F of lower weight. In a genetic algorithm (GA) this idea may be applied in the creation of initial solutions or as crossover and mutation build new solutions [2, 6, 13, 14]. This paper examines weight biased crossover operators in GAs for graph problems like those listed above. We investigate four edge selection strategies random, greedy, according to probabilities inversely proportional to edges weights, a) 20 40 60 80 100 (b) 20 40 60 80 100 ....

....the current city to an unvisited city. Append the city at the other end of the selected edge to the tour; this city becomes current. The details of the edge selection step distinguish the eight crossover operators. The most aggressive of these, GX2, is the very greedy crossover described in [6]. Many researchers have described crossovers that conform to the outline above. Perhaps the first was proposed by Grefenstette et al. 4] their heuristic crossover selects the shortest parental edge to an unvisited city, if such an edge exists. Otherwise, it chooses the next city at random from ....

B. A. Julstrom. Very greedy crossover in a genetic algorithm for the Traveling Salesman Problem. In K. M. George, J. H. Carroll, E. Deaton, D. Oppenheim, and J. Hightower, editors, Proceedings of the 1995.

....edges predominate in optimal solutions to such problems. This suggests that in EAs, crossover and mutation, which build representations of novel solutions from existing representations, should be biased so as to favor edges of lower weight. Several researchers have investigated such schemes [3, 4, 9, 10]. This work is supported by the Austrian Science Fund (FWF) grant P13602 INF. Among them, Julstrom and Raidl examined weight biased crossover operators in EAs for the TSP and the d MSTP [5] favoring low weight edges improved the performance of these algorithms. We extend that inquiry to ....

B. A. Julstrom. Very greedy crossover in a genetic algorithm for the Traveling Salesman Problem. In K. M. George, J. H. Carroll, E. Deaton, D. Oppenheim, and J. Hightower, editors, Proceedings of the 1995 ACM Symposium on Applied Computing, pages 324--328. ACM Press, 1995.

....that of three other heuristics. We must of course be cautious when drawing conclusions from only ten trials for each case; only large differences in performance are meaningful here. These results are competitive with those of other recent one population GAs for TSP that do not employ local search [4, 3, 13]. They do not, however, approach the results achieved on larger problems by algorithms with multiple interacting populations, local search operators, or both [1, 2, 7] Which of the insertion heuristics deserves further investigation as the decoding algorithm Both farthest insertion 1 and ....

Bryant A. Julstrom. Very greedy crossover in a genetic algorithm for the Traveling Salesman Problem. In K. M. George, Janice H. Carroll, Ed Deaton, Dave Oppenheim, and Jim Hightower, editors, Applied Computing 1995: Proceedings of the 1995 ACM Symposium on Applied Computing, pages 324--328, New York, 1995. ACM Press.

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