P. S. Gabbert, D. E. Brown, C. L. Huntley, B. P. Markowicz, and D. E. Sappington. A system for learning routes and schedules with genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 430--436, University of California, San Diego, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....problem solving strategy, based loosely on Darwinian evolution, that has been successfully used for a large number of scheduling and optimization In X. Yao (Editor) Proceedings of The Australia Japan Joint Workshop on Intelligent and Evolutionary Systems , Pages 55 64, Canberra, 1997 problems[2, 9]. Genetic algorithms are generally associated with long computation times and great uncertainty about how long a computation will take. Consequently they are not normally considered for real time problems, such as the optimal scheduling of aircraft landing times. Despite the perceived ....

P. S. Gabbert, D. E. Brown, C. L. Huntley, B. P. Markowicz, and D. E. Sappington. A system for learning routes and schedules with genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 430--436, University of California, San Diego, 1991.

....Bersini, Hugues, 89] Biegel, John E. 178] Bierwirth, Christian, 128, 179, 180] Bloebaum, C. L. 129] Blume, C. 102] Blume, Christian, 109] Bonnet, J erome, 170] Bouffouix, S. 195] Bourdon, K. 83] Bowden, Royce O. 81] Breuer, P. 83] Brind, C. 281] Brown, Donald E. [190, 283] Bruns, Ralf, 181, 182, 183] Burks, Christian, 271, 272] Cai, X. 142] Cartwright, H. M. 110] Cartwright, Hugh M. 185, 270] Caskey, Kevin Richard, 186] Cavalier, 123] Chan, Heming, 61] Chan, K. C. 23, 64] Chan, W. T. 115, 140] Chang, I. 201] Changshui, Zhang, 154] ....

....[114, 198, 199] Fogarty, Terence C. 79, 91] Forrest, Stephanie, 271, 272] Fourman, Michael P. 35, 36] Fox, B. R. 273] Fox, Geoffrey C. 200] Fujita, Kikuo, 37] Fukuda, T. 212] Fuquay, D Ann, 261] Furuhashi, Takeshi, 13, 259, 260] Fwa, T. F. 115, 140] Gabbert, Paula S. [190] Gen, Mitsua, 280] Gen, Mitsuo, 111, 117, 147, 149, 151, 201] Germay, Noel, 202] 14 Gerys, D. 249] Glasmacher, Klaus, 41] Glesner, M. 203] Glover, David E. 39] Gold, Sonke Sonnich, 60] Gonzalez, Carlos, 184] Gorrini, V. 148] Goulter, I. C. 31] Greenwood, Garrison W. ....

[Article contains additional citation context not shown here]

Paula S. Gabbert, Donald E. Brown, Christopher L. Huntley, Bernard P. Markowicz, and David E. Sappington. A system for learning routes and schedules with genetic algorithm. In Belew and Booker [298], pages 422--429. ga:DEBrown91a.

....at any time and will always have a result available but will produce a better result given more time. Genetic algorithms[2] 3] are a problem solving strategy, based loosely on Darwinian evolution, that has been successfully used for a large number of scheduling and optimization problems[4], 5] Genetic algorithms are generally associated with long computation times and great uncertainty about how long a computation will take. Consequently they are not normally considered for real time problems, such as the optimal scheduling of aircraft landing times. Despite the perceived ....

P. S. Gabbert, D. E. Brown, C. L. Huntley, B. P. Markowicz, and D. E. Sappington, "A system for learning routes and schedules with genetic algorithms," in Proceedings of the Fourth International Conference on Genetic Algorithms, (University of California, San Diego), pp. 430--436, 1991.

....saw an explosion in the number of GA researchers. Consequently there has been a fair volume of very recent work on scheduling. Some of this will be outlined now, before going on to more advanced techniques. Gabbert et al. successfully applied a GA to transport (train) scheduling and routing [13]. Unlike other approaches, they were able to use modifiable complex cost models, avoiding most of the standard simplifications. They are confident of being able to scale up their prototype systems. This seems to be a good example of exploiting the strengths of GAs to handle those aspects of a ....

P. Gabbert et al. A system for learning routes and schedules with genetic algorithms. In R. K. Belew and L. B. Booker, editors, Proceedings of the Fourth Intl. Conf. on Genetic Algorithms, ICGA-91, pages 430--436. Morgan Kaufmann, 1991.

....for heavily constrained non convex problems like the Rompnet problem using a new variant of the Genocop system. Although a lot of work has been done for timetabling, for example for job shop scheduling, for assigning teachers to classes ( BUM91] CLS91] and also for scheduling trains ( AMP93] [GBH91]) we have not seen the requirement that every hour the schedule should be the same. As outlined before, this causes the search space to become non convex. Usually when non convex search spaces are encountered one uses filter or repair algorithms to remain in the valid search space. For the Rompnet ....

Gabbert, P.S., Brown, D.E., Huntley, C.L., Markowicz, B.P. & Sappington, D.E. A System for Learning Routes and Schedules with Genetic Algorithms. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 430-436, Morgan Kaufmann, 1991.

....problems which have been solved using an evolutionary approach include the following : ffl Job shop scheduling [1, 7] This problem is a very difficult problem, in terms of optimisation, and has become something of a benchmark for optimisation algorithms. ffl Learning routes and schedules [8]. This problem involved the GA having to route and schedule trains in a rail network. This approach was shown to be useful, as well as feasible. ffl Resource scheduling [23] Syswerda and Palmucci were faced with the problem of scheduling the use of a flight simulator equipment. They demonstrated ....

P. S. Gabbert, D. E. Brown, C. L. Huntley, B. P. Markowicz, and D. E. Sappington. A system for learning routes and schedules with genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 430--436, University of California, San Diego, 1991.

....strings. The algorithm behaves as a random manipulator of the building blocks or schemata (singular schema) which comprise the strings. These schemata are string sub sections that may or may not be contiguous. For example, the string S= 5, 8, 2, 3, 10, 9, 4, 1, 7, 6 contains the schemata [2, 3, 10], 4, 1, 7, 6, 8, 2] and [5, 8, 2, 3, 10] among numerous others. It also contains schemata such as [ 3, 4, 1, 7] or [2, 10, 4] where indicates that the value which appears in the starred location is unimportant. All these schemata are manipulated by the algorithm. Suppose the ....

....behaves as a random manipulator of the building blocks or schemata (singular schema) which comprise the strings. These schemata are string sub sections that may or may not be contiguous. For example, the string S= 5, 8, 2, 3, 10, 9, 4, 1, 7, 6 contains the schemata [2, 3, 10] 4, 1, 7, 6, [8, 2] and [5, 8, 2, 3, 10] among numerous others. It also contains schemata such as [ 3, 4, 1, 7] or [2, 10, 4] where indicates that the value which appears in the starred location is unimportant. All these schemata are manipulated by the algorithm. Suppose the calculation has progressed ....

[Article contains additional citation context not shown here]

Gabbert P.S., Brown, D.E., Huntley, C.L., Markowicz, B.P., & Sappington, D.E., A System for Learning Routes and Schedules with Genetic Algorithms. Proceedings of the 4th International Conference on Genetic Algorithms, Morgan Kaufmann,San Mateo Ca., 1991, 430-6.

....and the number of values for the variables. It will only perform well with small schedules. It is not considered. GA has successfully been applied in many scheduling problems ( Abramson 88] Bagchi et al. 91] Cleveland and Smith 89] Corne et al. 92] Davis 85] Feldman and Golumbic 90] Gabbert et al. 91] Juliff 93] Murphy 87] Nakano 91] Schaffer and Morishima 87] Syswerda and Palmucci 91] Although it cannot guarantee to return the optimal solution, research shows that the solution returned is reasonably close to optimal if the solution itself is not optimal. GA is a reasonable choice ....

Paula S. Gabbert, Donald E. Brown, Christopher L. Huntley, Bernard P. Markowicz and David E. Sappington (1991). A System for Learning Routes and Schedules with Genetic Algorithms. Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, Publishers.

....saw an explosion in the number of GA researchers. Consequently there has been a fair volume of very recent work on scheduling. Some of this will be outlined now, before going on to more advanced techniques. Gabbert et al. successfully applied a GA to transport (train) scheduling and routing [11]. Unlike other approaches, they were able to use modifiable complex cost models, avoiding most of the standard simplifications. They are confident of being able to scale up their prototype systems. This seems to be a good example of exploiting the strengths of GAs to handle those aspects of a ....

P. Gabbert et al. A system for learning routes and schedules with genetic algorithms. In R. K. Belew and L. B. Booker, editors, Proceedings of the Fourth Intl. Conf. on Genetic Algorithms, ICGA-91, pages 430--436. Morgan Kaufmann, 1991.

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