James C. Bean. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154-160, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....analysis of DNA [98] among others. The use of special representations and operators is, with no doubt, quite useful for the intended application for which they were designed, but their generalization to other (even similar) problems is by no means obvious. 3.2 Random keys James C. Bean [8, 9] proposed a special representation called random keys encoding which (in contrast with the approaches reported in Davis book) is used to eliminate the need of special crossover and mutation operators in certain sequencing and optimization problems (e.g. job shop scheduling, parallel machine ....

James C. Bean. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154-160, 1994.

....analysis of DNA [98] among others. The use of special representations and operators is, with no doubt, quite useful for the intended application for which they were designed, but their generalization to other (even similar) problems is by no means obvious. 3.2 Random keys James C. Bean [8, 9] proposed a special representation called random keys encoding which (in contrast with the approaches reported in Davis book) is used to eliminate the need of special crossover and mutation operators in certain sequencing and optimization problems (e.g. job shop scheduling, parallel machine ....

James C. Bean. Genetics and random keys for sequencing and optimization. Technical Report TR 92-43, Department of Industrial and Operations Engineering, The University of Michigan, 1992.

....to continuous optimization problems. The main operator of ES is mutation, whereas recombination is only important for the self adaption of the strategy parameters. Random network keys (NetKeys) have been proposed by [5] as a way to represent trees with continuous variables. This work was based on [6] and allows to represent a permutation by a sequence of continuous variables. In this work we investigate the performance of ES for tree problems when using the continuous NetKey representation. Because ES have been designed for solving continuous problems and have shown good performance therein, ....

....(subsection 4.3) The paper ends with concluding remarks. 2 Network Random Keys This section gives a short overview about the NetKey encoding. Network random keys are adapted random keys (RKs) for the representation of trees. RKs allow us to represent permutations and were first presented in [6]. Like the LNB encoding [8] NetKeys belong to the class of weighted representations. Other tree representations are Pr ifer numbers [9] direct encodings [10] or the determinant encoding [11] When using NetKeys, a key sequence of 1 random numbers ri [0, 1] where i 0, 1 1 , represents a ....

J. C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, June 1992.

....on Darwin s theory of survival of the fittest in the process of evolution. Although a different terminology is used (see Table 1 for a list) the basic ideas are very similar to tabu search. A formal statement of the method we use is as follows (for a thorough overview, the reader is referred to [12, 13]) 1. As with Tabu search, the solutions of WSPT first, WLPT first, dynamic programming and dispatching rule D 3 are used to obtain the initial population. A constant population size of 4 is assumed. Initialize k=1. 2. Consider the k th generation. Find the most and least fit individual in ....

J. Bean, "Genetics and Random Keys for Sequencing and Optimization," ORSA Journal of Computing, Vol. 6, pp. 154-160, 1994.

....was unused. This results in a simple conjunctive representation. The basic Genesis program was modified in three primary ways: 1. Most GA s converge to a single optimum. The fitness evaluation was changed to a two step process, to encourage multiple optima, or niching. 2. Immigration was used [1, 2]; examples from the training dataset were converted into GA strings each generation. Empirical evidence suggests that this helps to maintain the diversity of the GA population and to increase coverage of the rule set. 3. A parameter was added to the fitness function to allow for penalizing ....

J. C. Bean. Genetics and random keys for sequencing and optimization. Technical Report TR-92-43, Department of Industrial and Operations Engineering, The University of Michigan,

....reports. The list is arranged in alphabetical order by the name of the institute. Institute for New Generation Computer Technology, 10, 95, 21, 22] Massachusets Institute of Technology, 103] Sandia National Laboratories, 35, 94] University of Maryland, 104, 105] University of Michigan, [16] total 10 reports in 5 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms and proteins. The list is arranged in alphabetical order by the name of the patent. ffl none 9 10 Genetic algorithms and proteins 4.6 Authors The following list contains ....

....genetic algorithms and proteins authors and references to their known contributions. Addis, Tom, 119] Aert, A. H. J. M. van, 24] Alander, Jarmo T. 36] Argos, Patrick, 42, 57, 85, 86] Arkin, A. P. 102] Asai, Kiyoshi, 10] Aspnas, Anders, 76] Bass, Michael B. 77] Bean, James C. [16] Beeson, Nicholas Welborn, 65] Blommers, Marcel J. J. 24, 25, 99] Bryson, James W. 78] Burks, C. 15] Burks, Christian, 18, 19] Buydens, Lutgarde M. C. 81, 25, 100, 101] Calabretta, R. 66] Caruthers, James M. 29, 31, 32] Cede no, Walter, 13] Cedeno, Walter, 87, 17] Chan, ....

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James C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, University of Michigan, Ann Arbor, Department of Industrial and Operations Engineering, 1992. y ga:Bean92a.

.... F ed erale de Lausanne, 189] Eindhoven University of Technology, 188] Friedrich Alexander Universitat Erlangen Nurnberg, 60] Limburg University, 236] Netrologic, 286] RWTH Aachen, 101] Royal Melbourne Institute of Technology, 17] University of Exeter, 38] University of Michigan, [269, 174] University of Strathclyde, 227] University of Virginia, 33] Universit e Libre de Bruxelles, 148] total 15 reports in 14 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms and manufacturing. The list is arranged in alphabetical order by the ....

....Ansari, Nirwan, 120, 207] Araki, Miyuhiko, 258] Arunkumar, S. 169] Ashmore, B. 40] Atlan, Laurent, 170] Awadh, B. 77] Awadth, Bahaa, 70] Aytug, Haldun, 71] Bac, Fam Quang, 171] Bagchi, Sugato, 172, 173] Banerjee, P. 49, 50] Baumgartner, Joseph P. 145] Bean, James C. [108, 269, 174] Beaty, Steven J. 175, 176, 177] Becker, B. D. 244] Benten, Muhammed S. T. 106] 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, ....

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James C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, University of Michigan, Ann Arbor, Department of Industrial and Operations Engineering, 1992. y ga:Bean92a.

.... Naval Ocean Systems Center, 32] Ochanomizu University, 863] Ruhr Universit at Mannheim, 789] Sandia National Laboratories, 402, 403] University of Cambridge, 992] University of Illinois, 687] University of Louisville, 422] University of Maryland, 1091, 1092] University of Michigan, [618] University of Vaasa, 908] VTT Automation, 509] total 21 reports in 16 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms in chemistry and physics. The list is arranged in alphabetical order by the name of the patent. Method and apparatus for ....

....Bartels, Christian, 288, 1121] Barton, G. W. 221] Barton, Geo rey W. 284] Bartton, G. W. 78] Baskeshki, K. 466] Bass, Michael B. 982] Bastos, R. C. 310] Batenburg, F. H. van, 631, 633, 639, 444] Battle, P. D. 53] Baudet, Philippe, 83] Baylay, Martin J. 1034] Bean, James C. [618] 20 Genetic algorithms in chemistry and physics Beckers, Mischa L. M. 264, 983, 597, 346, 114] Becks, K. H. 145] Becks, K. H. 889] Beeson, Nicholas Welborn, 951] Beiersd orfer, Susanne, 289, 1008, 208] Beigel, Richard, 608] Belew, Richard K. 443] Belmans, R. 842] Belmont Moreno, ....

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James C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, University of Michigan, Ann Arbor, Department of Industrial and Operations Engineering, 1992. y ga:Bean92a.

.... University of Dortmund, 67, 69, 96, 219, 6] University of Durham, 526] University of East Anglia, 234] University of Illinois at Urbana Champaign, 249, 250, 251, 252, 253, 254, 3, 256, 257, 458] University of Louisville, 705] University of Maryland, 507, 508] University of Michigan, [81, 82, 83] University of Sussex, 298, 299, 301, 302, 303, 304, 305, 306, 307] Universitat Gottingen, 528] Universitat Osnabruck, 412] total 81 reports in 45 institutes 4.5 Patents The following list contains the names of the patents of genetic algorithms of 1992. The list is arranged in ....

....68, 69, 70, 71, 72, 73, 74, 6] Bala, Jerzy W. 75, 149, 150] Balio, R. Del, 148] Baluja, Shumeet, 76, 438] Bank van der, Dirk Johannes, 722] Barclay, A. R. 497] Barth, N. H. 77] Baskaran, Subbiah, 78] Bassus, R. C. 79] Battiti, R. 80] Battle, S. A. 198] Bean, James C. [81, 82, 83] Becker, B. D. 18] Becker, Douglas E. 590, 591] Becks, K. H. 84] Bedau, M. A. 714] Beer, Randall D. 85] Belew, Richard K. 86, 87, 88] Ben Kiki, Oren, 142] Benson, R. 218] Bergman, Aviv, 89] Bersini, Hugues, 90, 91, 92, 10, 93, 94] Bessi ere, Pierre, 656, 658, 660] ....

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James C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, University of Michigan, Ann Arbor, Department of Industrial and Operations Engineering, 1992. y ga:Bean92a.

....old or inferior solutions in the population. A GA works iteratively by improving the initially random solutions creating subsequent generations of (generally) improving solutions. The GA of Tate and Smith [22] which has been enhanced through use of the random keys (RK) encoding of Norman and Bean [2, 13 15] and the adaptive penalty approach of Coit, Smith and Tate [5] was used. The random keys encoding eliminates the need for special purpose crossover and mutation operators to maintain encoding integrity for permutations. Each block layout is encoded as a string of floating point numbers indicating ....

Bean, J.C., "Genetics and random keys for sequencing and optimization," ORSA Journal on Computing, 1994 6, 154-160.

....that were not valid permutations as a result of crossover or mutation and an exterior penalty function for aspect ratio violations. To overcome potential feasibility problems in the department ordering problem, the representation in this paper uses the random keys (RK) encoding of Norman and Bean (Bean 1994, Norman and Bean 1997, Norman and Bean 1999, Norman and Bean in print) This encoding assigns a random U(0,1) variate, or random key, to each department in the layout and these random keys are sorted to determine the department sequence. Consider the thirteen department example of Figure 1. The ....

Bean, J. C., "Genetics and random keys for sequencing and optimization," ORSA Journal on Computing, 1994 6, 154-160.

....and Smith used a repair operator to fix strings that were not valid permutations as a result of crossover or mutation. To overcome potential feasibility problems in the department ordering problem, we have enhanced the representation through use of the random keys (RK) encoding of Norman and Bean [2, 7 9]. This encoding assigns a random U(0,1) variate, or random key, to each department in the layout and these random keys are sorted to determine the department sequence. Consider the thirteen department example of Figure 1. The chromosome of random keys given below, when sorted in ascending order, ....

Bean, J.C., "Genetics and random keys for sequencing and optimization," ORSA Journal on Computing, 1994 6, 154-160.

....preserve the validity of tours. This method can be used in the HGA but requires modification of the CMM. In lieu of this, conventional crossover operators can be used in conjunction with a special encoding of the population members. One such encoding is called a random keys encoding [46] 47] [48]. In this encoding, each tour is represented by a tuple of random numbers, one number for each city, with each number from [0; 1] After selecting a pair of tours, simple or uniform crossover can be applied, yielding two new tuples. To evaluate these tuples, sort the numbers and visit the cities ....

....we note that the scheme just presented can be adapted for application to other problems with similar constraints as the TSP. These include scheduling problems, vehicle routing, and resource allocation (a generalization of the 0 1 knapsack problem and the set partitioning problem) 46] 47] [48]. The HGA can be applied to these problems as well with a slight increase in complexity, yielding a solution to the real time GA disk scheduling problem of Turton et al. 5] But a major difference is that each member selected in Turton et al. s scheme is selected from a very small set of members ....

J. Bean, "Genetics and random keys for sequencing and optimization," ORSA Journal on Computing, vol. 6, pp. 154--160, 1994.

....general enough to handle constraints that are not linear. 2.2 Problem Specific Codings Problem specific codings apply normal genetic operators to strings represented by schemes designed in such a way that invalid strings cannot be represented. This technique has been applied to the TSP by Bean [1] with the use of random keys. A tour is represented by a list of random numbers, e.g. 0.50 0.23 0.79 0.43) This string is decoded into a valid tour before evaluation. For example, 0.50 0.23 0.79 0.43) would decode into the permutation (2 4 1 3) by first sorting the random numbers and then ....

James C. Bean. Genetics and random keys for sequencing and optimization. Department of Industrial and Operations Engineering, Univiersity of Michigan, Ann Arbor, Michigan, June 1992.

....operators that preserve the validity of tours. This method can be used in the HGA but requires modification of the CMM. In lieu of this, conventional crossover operators can be used in conjunction with a special encoding of the population members. One such encoding is called a random keys encoding [11, 37]. In this encoding, each tour is represented by a tuple of random numbers, one number for each city, with each number from [0; 1] After selecting a pair of tours, simple or uniform crossover can be applied, yielding two new tuples. To evaluate these tuples, sort the numbers and visit the cities ....

....ne 1) Finally, we note that the scheme just presented can be adapted for application to other problems with similar constraints as the TSP. These include scheduling problems, vehicle routing, and resource allocation (a generalization of the 0 1 knapsack problem and the set partitioning problem) [11, 37]. The HGA can be applied to these problems as well with a slight increase in complexity. 4.6 Other NP Complete Problems In this section we explore the exploitation of polynomial time reductions between instances of NP complete problems. Developing a GA to solve any NP complete problem (e.g. SAT, ....

J. Bean. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6:154--160, 1994.

....right across the chromosome. Instead we propose an alternative approach to order based problems, using the Random Key Encoding. This allow us to use standard Crossover and Mutation operators and so preserve the pipelined nature of the dataflow. Random Key encoding is often credited to Bean [31] but similar schemes predate this work [32] Its usefulness to Hardware based GAs was first highlighted by Scott [26] The method involves encoding a solution using random numbers. Each random number is associated with a traditional gene in an order based representation, such as a city in TSP. ....

J. C. Bean. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154--160, 1994.

....it is not clear what kind of penalty function to apply or whether legal blocks within these individuals would be useful building blocks. We studied the remaining two approaches, first trying the sorted order representation, in which all solutions map to a legal permutation order [ Syswerda 1989) (Bean 1992)] We then studied the performance of the simple permutation representation in combination with two special purpose recombination operators, edge recombination [ Starkweather et al. 1991) and order crossover [ Davis 1985) In addition to recombination, we explore both bit and position mutation ....

....this type of representation provides the isolation of problem specific information from which the genetic algorithm derives its generality. For this reason, we chose to try the sorted order representation [ Schaffer et al. 1989) Syswerda 1989) also referred to as the random key representation [(Bean 1992)] The sorted order representation provides a rather complex mapping from the individual to the permutation ordering. The two requisite properties for a legal ordering are that all fragments be present in the ordering and that there be no duplication in the ordering. These properties are ensured ....

Bean, J. C. 1992. Genetics and random keys for sequencing and optimization. Technical Report 92-43, The University of Michigan.

....trees. However when we implemented this solution, we saw that it is a very inefficient way to handle our problem, because about half of the population were formed with such invalid parse trees and we had to discard all of them. Random keys, developed by Bean and Norman could be another solution [1, 2]. Random keys are developed to overcome the difficulty of genetic algorithms maintaining feasibility from parent to off spring. To illustrate the use of random keys, consider a simple genetic algorithm approach to the traveling salesman problem. A candidate solution to a TSP is a tour through n ....

Bean J.C.: Genetic and Random Keys for Sequencing and Optimization. Dept. of Industrial & Operations Engineering, Univ. of Michigan, Technical Report (June 1992) 92-43

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J. C. Bean. Genetics and random keys for sequencing and optimization. Technical Report 92-43, Dept. of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, 1992. 38

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J. C. Bean. Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6:154--160, 1994.

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