R. Hinterding: Mapping, Order-independent Genes and the Knapsack Problem, in Proc. of the 1st IEEE International Conference on Evolutionary Computation 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... More examples of heuristic algorithms for the MKP can be found in [15] 16] 19] 23] A comprehensive review on exact and heuristic algorithms is given in [7] 8] In the last few years, Genetic Algorithms (GAs) have shown to be very well suited for solving larger Knapsack Problems, see [12], 14] 18] 22] 20] 7] 8] and general 0 1 integer programming problems [21] The next section gives a survey of these GAs. In section 3 a new hybrid GA is introduced which starts with an initial population of pre optimized solutions determined by a heuristic based on the LP relaxed ....

....guarantees that only feasible solutions are generated. But on the other hand, a disadvantage of this representation seems to be the fact that a specific solution can be encoded in many ways and the search space is much larger than when using the string representation. Hinterding presented in [12] a GA with such an order based representation for the uni dimensional KP. He used a uniform order based crossover (see [3] 11] 17] as recombination operator and realized that disallowing duplicates in the population significantly improves results. Furthermore, he compared this GA to another ....

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R. Hinterding: Mapping, Order-independent Genes and the Knapsack Problem, in Proc. of the 1st IEEE International Conference on Evolutionary Computation 1994.

....heuristic, the new GA approach outperforms previous hybrid GAs with traditional encodings on nearly all test problems. 2. Prior approaches Several researchers have developed successful GAs for the KP and the more di#cult multi constraint KP, including Chu [3] Chu and Beasley [4] Hinterding [9], Khuri et al. 14] Olsen [17] and Raidl [19] Falkenauer [6] presented a hybrid GA for the BPP. In [20] Raidl and Kodydek observed that these GA approaches can be divided into two categories according to the solution encoding techniques: Some algorithms use direct encoding (DE) meaning that ....

Hinterding R.: Mapping, Order-independent Genes and the Knapsack Problem, in Proc. of the 1st IEEE Int. Conference on Evolutionary Computation 1994.

....Since our goal is to characterize several decoder based EAs concerning this last aspect, we proceed by a brief introduction and empirical comparison of them. 2. 1 Permutation Based EA The permutation based EA (PBEA) has been proposed by Hinterding for the (unidimensional) knapsack problem [9] and can easily be adapted to the MKP [17, 24] A solution candidate is represented by a permutation # : J J of the items. The decoder starts with the feasible solution x = 0, 0) and traverses all variables x j in the order determined by #, increasing the corresponding variable from 0 ....

R. Hinterding: Mapping, Order-independent Genes and the Knapsack Problem, in Proc. of the 1st IEEE Int. Conference on Evolutionary Computation, Orlando, FL, pp. 13 -- 17, 1994

....of these heuristics is very limited if they are applied to MKPs where both m and n are large. See [3, 4] for a comprehensive review on exact and heuristic algorithms. In the last years, GAs have shown to be well suited for finding high quality solutions to also larger knapsack problems, see [3, 4, 6, 7, 8, 15, 18, 20, 21]. In [18] Raidl observed that these GA approaches can be divided into two categories according to the solution encoding techniques. Some algorithms use direct encoding, meaning that a chromosome of the GA contains a gene for each item indicating directly if the item is supposed to be packed into ....

R. Hinterding: Mapping, Order-independent Genes and the Knapsack Problem, in Proc. of the 1st IEEE Int. Conf. on Evolutionary Computation, Orlando, FL, pp. 13--17, 1994.

....to job shop scheduling problems and thus proved his modication to be more ecient than the classic crossover operation. Cheung [9] presented and analyzed dierent neural networks for dierent scheduling problems. Some other applications of GA to dierent NP hard pure IP problems can be found in [11, 14 16, 25]. All of these examples show the successes of GA applied to NP hard pure IP problems. Thus, we choose GA to be the rst component of our integrated method. Although there have been successes in applying GA to pure IP problems, it is a dierent situation when we use GA to solve linear MIP problems. ....

Hinterding, R. (1994). Mapping, Order-Independent Genes and The Knapsack Problem, Proceedings of The 13 17.

....The list is arranged in alphabetical order by the name of the institute. Australian Defence Force Academy, 76] C.S.I.R.O. 217] Royal Melbourne Institute of Technology, 227] University of Adelaide, 234, 239] University of New South Wales, 247, 248] Victoria University of Technology, [226, 229] total 9 reports in 6 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms in Australia. The list is arranged in alphabetical order by the name of the patent. none Authors 13 4.6 Authors The following list contains all genetic algorithms in ....

....[339] Hedges, S. 262] Hendtlass, T. 159, 177] Hendtlass, Tim, 58, 281] Hibbert, D. Brynn, 222, 223, 224, 225] Hibbs, R. 70, 278] Hiden, H. G. 150] Higgins, A. 165] Higuchi, T. 144] Hill, David J. 193] Hingston, Philip, 61] Hinterding, R. 81] Hinterding, Robert, [14, 93, 97, 166, 174, 195, 226, 229] Ho, J. S. 347] Hobbs, Matthew F. 343, 260] Honeyman, Marco, 209] Hsu, Chen Chien, 155] Hu, X. 115] Huat, Tan Thiam, 295] Hunter, A. 262] Hush, Noel S. 207] Hutter, Michael C. 207] Huxford, Stephen, 154, 192, 200] Hwang, Chong Sun, 301, 300] Ishigami, Hideyuki, 308] ....

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Robert Hinterding. Mapping, order-independent genes and the knapsack problem. Technical Report TR 35 COMP8, Victoria University of Technology, Department of Computer and Mathematical Sciences, 1993. ga:Hinterding93a.

....[339] Hedges, S. 262] Hendtlass, T. 159, 177] Hendtlass, Tim, 58, 281] Hibbert, D. Brynn, 222, 223, 224, 225] Hibbs, R. 70, 278] Hiden, H. G. 150] Higgins, A. 165] Higuchi, T. 144] Hill, David J. 193] Hingston, Philip, 61] Hinterding, R. 81] Hinterding, Robert, [14, 93, 97, 166, 174, 195, 226, 229] Ho, J. S. 347] Hobbs, Matthew F. 343, 260] Honeyman, Marco, 209] Hsu, Chen Chien, 155] Hu, X. 115] Huat, Tan Thiam, 295] Hunter, A. 262] Hush, Noel S. 207] Hutter, Michael C. 207] Huxford, Stephen, 154, 192, 200] Hwang, Chong Sun, 301, 300] Ishigami, Hideyuki, 308] ....

.... transputers, 218] Turbo C, 310] inductive learning, 314] industry petroleum, 200] infrared imaging, 215] inverse problems neuromagnetism, 266] inversion problems, 20] seismic, 111] seismology, 74] iterated prisoner s dilemma, 72] job shop scheduling, 243] knapsack problem, [226, 14, 81] knowledge acquisition, 161] Subject index 19 knowledge aquisition, 170] laminates, 282] land mines, 215] languages Farsi, 298] layout design, 242, 15, 27, 36, 120, 196] liver disease, 253] load balancing, 300] load ow, 138, 171, 204] load shedding, 119] load ow problem, ....

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Robert Hinterding. Mapping, order-independent genes and the knapsack problem. In ICEC'94 [351], pages 13-17. ga94aHinterding.

.... New South Wales, 1091, 1092] University of North Carolina at Charlotte, 715, 716] University of Pensylvania, 550] University of Sheffield, 284] University of Strathclyde, 201, 704, 708, 709] University of Sussex, 438, 450] University of Tulsa, 183] Victoria University of Technology, [466, 527] Vrije Universiteit, 247] total 100 reports in 68 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms of 1993. The list is arranged in alphabetical order by the name of the patent. ffl none Authors 15 4.6 Authors The following list contains ....

....[459] Hern aez, I. 725] Hessburg, T. 653] Heuvel, H. M. 644] Hibbert, D. Brynn, 460, 461, 462, 463] Hidalgo, D. 725] Hightower, Ron, 312] Higuchi, Tetsuya, 1104, 1107, 1110, 1115, 574] Higuchi, T. 464] Hindi, K. S. 425] Authors 19 Hines, Evor L. 465] Hinterding, Robert, [466, 527] Hiramatsu, Atsushi, 1089] Hirose, Tetsuya, 584] Hirota, Y. 320] Hirsh, Joel, 872] Ho, J. S. 401] Hochmuth, D. H. 239] Hofferer, Max, 467] Hoffmann, Karl Heinz, 393, 394] Holland, John H. 468, 469, 470] Holsapple, Clyde W. 471] Homaifar, Abdollah, 472, 627] Honavar, ....

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Robert Hinterding. Mapping, order-independent genes and the knapsack problem. Technical Report TR 35 COMP8, Victoria University of Technology, Department of Computer and Mathematical Sciences, 1993. ga:Hinterding93a.

....can be very slow. Falkenauer Page: 1991) uses a grouping chromosome to overcome this problem. Here each gene represents a group of items, and hence crossover works with groups of items rather than a list of single items. In this paper we build on earlier work (Hinterding Juliff 1993; Hinterding 1994) to develop GAs for the CSP using two different mappings. Hinterding Juliff implemented a multi chromosome GA for cutting stock, and we dramatically improve and extend on their work. Hinterding (1994) explored two different mappings for solving the Knapsack problem. We extend the use of these ....

....than a list of single items. In this paper we build on earlier work (Hinterding Juliff 1993; Hinterding 1994) to develop GAs for the CSP using two different mappings. Hinterding Juliff implemented a multi chromosome GA for cutting stock, and we dramatically improve and extend on their work. Hinterding (1994) explored two different mappings for solving the Knapsack problem. We extend the use of these mappings to the cutting stock problem. Falkenauer (1994) has extended his bin packing GA by using local optimisation to improve his results dramatically and we compare our GAs to his. We restrict ....

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Hinterding, Robert, Mapping, Order-independent Genes and the Knapsack Problem, Proceedings of the First IEEE Conference on Evolutionary Computation (ICEC'94), pp. 13-17, Orlando 1994.

....This is much larger than before. ffl We can now explore the effects of mutation on a gene. Before we could only flip a bit. A number of GAs with large or very large alphabets have been implemented, examples are: Order based GAs (Davis, 1985) and Group based GAs (Falkenauer Delchambre, 1992; Hinterding, 1994). These implementations have g1 g2 g3 g4 g5 g6 g7 g8 g1 g1 g1 g1 g2 g2 g2 g2 Traditional GA: 2 variables, each 4 bits; 8 genes Variables as genes: 2 variables, each 4 bits; 2 genes Figure 1: Gene Representation Table 1: Mutation statistics Decoding Mean Mean Dev. Std Dev. Binary 0 3.75 4.61 Gray ....

Hinterding, Robert. 1994. Mapping, Order-independent Genes and the Knapsack Problem. In: Proceedings of the First IEEE Conference on Evolutionary Computation. Orlando, Florida: IEEE Press. pp. 13-17.

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