D.B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 63:111--114, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....from being further explored. In this paper, an adaptively adjusted lower bound is proposed which supports better fine tune searches and spreads out exploration as well. 1 Introduction Evolutionary programming (EP) 1] has been applied to many optimization problems successfully in recent years [2, 3, 4]. A global optimization problem can be formalised as a pair (S; f) where S R is a bounded set in R and f : S R is an n dimensional real valued function. The problem is to find a vector x min 2 S such that f(x min ) is a global minimum on S. More specifically, it is required to find an x ....

D.B. Fogel. Applying evolutionary programming to selected control problems. Computers & Mathematics with Applications, 27(11):89--104, 1994.

.... system[7] Themethodsusedare: AGeneticAlgorithmbasedonclusteringandsharing(GACS) 11] ABitClimbingalgorithm(BClimb) 12] ADynamicHillClimbingalgorithm(DHClimb) 13] ApopulationbasedIncrementallearningalgorithm(PBIL) 14] SimulatedAnnealing(SA) 15] EvolutionaryProgramming(EP)[16] . EvolutionStrategybasedontheearlierworkofBack,HoffmeisterandSchwefel(ES) 17] Inthenextthreesectionwesetouttheremultileveloptimizationstrategies:Sequential,GraduallyMixed, andTotallyTotallyMixedstrategies. SequentialMultilevelOptimization Inthisapproach,theoptimiza ....

Fogel,"Applyingevolutionaryprogrammingtoselectedtravelingsalesmanproblems,"Cyberneticsand Systems,24(1)pp.27-36(1993).

.... Eil75 (75 city problem) 535 (542.37) 3,480] 545 (N A) 80,000] 542 (549.18) 325,000] 580 (N A) 173,250] 535 (N A) KroA100 (100 city problem) 21,282 (21,285.44) 4,820] 21,761 (N A) 103,000] N A (N A) N A] N A (N A) N A] 21,282 (N A) Results using EP are from [15], and those using GA are from [41] for Eil50, and Eil75, and from [6] for KroA100. Results using SA are from [29] Eil50, Eil75 are from [14] and are included in TSPLIB with an additional city as Eil51.tsp and Eil76.tsp. KroA100 is also in TSPLIB. The best result for each problem is in boldface. ....

.... worse solution using real valued distance as compared with EP, but ACS only visits 1,830 tours, while EP used 100,000 such evaluations (although it is possible that EP found its best solution Dorigo and Gambardella Ant Colony System 14 earlier in the run, this is not specified in the paper [15]) B. ACS on some bigger problems When trying to solve big TSP problems it is common practice [28] 35] to use a data structure known as candidate list. A candidate list is a list of preferred cities to be visited; it is a static data structure which contains, for a given city i, the cl closest ....

D. Fogel, "Applying evolutionary programming to selected traveling salesman problems," Cybernetics and Systems: An International Journal, vol. 24, pp. 27--36, 1993.

....instances. Surprisingly, one of the conceptually most simple algorithms shows the best performance on most of the instances. 1 Introduction The Traveling Salesman Problem (TSP) has played a central role in the development of many nature inspired heuristics like Evolutionary Computation [7, 9, 8, 28, 30], Ant Colony Optimization [5, 4] Neural Networks [1, 12] or Simulated Annealing [16, 3, 17] to name just the most important ones. Currently, for several such approaches very good performance is reported. Examples are the genetic algorithms by Merz and Freisleben [8, 21] Walters [29] Nagata and ....

D. B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 24:27--36, 1993.

....It works very well for some problems, but not for others. EP is simpler than GAs since it only uses mutation. It has been applied to a number of numerical and combinatorial problems with success [13] It has been shown to be more ecient than GAs for some numerical optimization problems [21, 22]. In this paper, we propose an EP algorithm with only a swap mutation operator which outperforms GAs for one dimensional CSPs with and without contiguity. The mutation in our EP is designed using the concept of the distance between a parent and its o spring. The distance is de ned as the ....

D. Fogel, \Applying evolutionary programming to selected control problems," Computers & Mathematics with Applications, vol. 27, no. 11, pp. 89-104, 1994.

....amount of time. Genetic algorithms (GAs) 12] and evolutionary programming (EP) 13] are two major classes of EAs. While GAs have been applied to the cutting and packing problems by many researchers [6, 14, 15, 16, 17, 18] EP s application in combinatorial optimization is relatively limited [19, 20]. Crossover is the primary search operator in GAs. It combines sound building blocks from di erent parents and passes them to their o spring. However, the e ectiveness of crossover as the primary search operator is problem dependent. It works very well for some problems, but not for others. EP ....

D. Fogel, \Applying evolutionary programming to selected traveling salesman problems," Cybernetics and Systems, vol. 24, pp. 27-36, 1993.

....[12] Arti cial Intelligence Review, 93] Autom. Prod. Inform. Ind. France) 30] Biological Cybernetics, 99, 100, 101, 102, 115, 119, 129] Br. Telecommun. Eng. UK) 14] Complex Systems, 126] Computers Industrial Engineering, 23] Cybern. Syst. USA) 81] Cybernetics and Systems, [121] Discrete Applied Mathematics, 19] Electronics Letters, 28] European Journal of Operational Research, 53, 87] Europhysics Letters, 107] Evolutionary Computation, 112] Future Generation Computer Systems, 117] IEEE Transactions on Computer Aided Design of Integrated Circuits and ....

....[69] Drummond, Lucia M.A. 39] Dzubera, J. 17] Ebeling, Werner, 106, 107] Eiben, Agoston, 50] Eigen, Manfred, 115] Engst, Norbert, 150] Falco, I. De, 114] Faulkner, Graeme, 34] Fernandez Villanacas, J. L. 14] Flores, B. 148] Fogarty, Terence C. 117, 118] Fogel, David B. [119, 120, 121, 122] Freisleben, Bernd, 54, 76] Fujikawa, H. 83] Fujikawa, Hideji, 49] Fukuda, Toshio, 58, 88] Fuquay, D Ann, 157] Gambardella, Luca M. 69] Gammack, John G. 117] Garigliano, Roberto, 141] Gause, Donald C. 21] Gen, Mitsuo, 23] Gold, S onke Sonnich, 150] Goldberg, David E. 123] ....

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David B. Fogel. Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems, 24(1):27-36, January-February 1993. yFogel/bib ga:Fogel93f.

.... genetic algorithm (GA) evolutionary programming (EP) and simulated annealing (SA) In Table 3 we report the best integer tour length, the best real tour length (in parentheses) and the number of tours required to find the best integer tour length (in square brackets) Results using EP are from (Fogel, 1993) and those using GA are from (Bersini, Oury and Dorigo, 1995) for KroA100, and from (Whitley, Starkweather and Fuquay, 1989) for Eil50, and Eil75. Results using SA are from (Lin, Kao and Hsu, 1993) Eil50 and Eil75 are from (Eilon, Watson Gandy and Christofides, 1969) and are included in TSPLIB ....

Fogel D., 1993. Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems: An International Journal, 24, 27--36.

....is to find a shorted closed tour through a given set of n cities with known inter city distances such that each city is visited exactly once and the tour ends at the start city. The TSP has played a central role in the development of many nature inspired heuristics like Evolutionary Computation [6, 7, 19, 22, 28], Ant Colony Optimization [4, 25] Neural Networks [1, 10] or Simulated Annealing [14, 15] to name just the most important. Currently, for several such approaches very good performance is reported. Examples are the genetic algorithms (GAs) by Merz and Freisleben [7, 19] Walters [28] Nagata and ....

D. B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 24:27--36, 1993.

.... [478] Computers and Geotechnics, 938] Computers Industrial Engineering, 197] Computers Mathematics with Applications, 60, 91, 260, 517] Computers Operations Research, 624, 945] Cryptologia, 689, 954, 956] Current Opinion in Structural Biology, 876] Cybernetics and Systems, [298, 599] Denshi Gijutsu Sogo Kenkyusho Iho, 464] Discrete Applied Mathematics, 41] Doboku Gakkai Rombun Hokokushu, 721, 967] Dr. Dobb s Journal, 169, 955] Electronics Letters, 171, 508, 822] Eng. Comput. UK) 840] Engineering Applications of Artificial Intelligence, 558] Engineering ....

....File, P. E. 1053] Filho, J. R. 55, 277, 278] Filipic, Bogdan, 279, 280, 281] Fleming, Peter J. 282, 283, 284, 285, 286, 287, 288] Fleurent, Charles, 273] Flockton, Stuart J. 662, 663, 664] Floreano, Dario, 289] Fogarty, Terence C. 290, 291, 292, 293, 294] Fogel, David B. [214, 295, 296, 3, 297, 298, 299, 300, 301, 302, 303, 304] Fogel, Gary B. 355] Fogel, Lawrence J. 297] Fonseca, Carlos M. 282, 283, 284, 285, 286, 287, 288, 305] Foote, Bobbie, 501] Forrest, Stephanie, 306, 307, 308, 309, 310, 311, 312, 313] Fortuna, L. 156, 157, 158] Fox, B. L. 314] Foy, M. 315] Franco, Aurali B. 854] Franich, R. ....

[Article contains additional citation context not shown here]

David B. Fogel. Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems, 24(1):27--36, January-February 1993. y(Fogel/bib) ga:Fogel93f.

....that assisting the evolutionary operators through the use of cleanup gives better performance on this evolutionary encoding. The implication of these findings run contrary to the apparent consensus towards a reduction in the number of genetic operators required [1] by a genetic system for the TSP [2, 3]. In these works Fogel concluded that mutation alone was sufficient for this encoding of the TSP problem, rejecting the crossover operator because of its tendency to introduce invalid tours into the population. We have shown that by using the cleanup operator in conjunction with crossover we can ....

.... to gas pipeline flow control systems [5] The travelling salesman problem is a well known member of the NP Complete class of problems [6] Over the last number of years it has returned to prominence with research conducted on providing near optimal solutions to large TS problems by Fogel [7, 3] and Lawler [8] Although these GA s have undergone some alterations, in particular to the techniques of the crossover, selection [9] and the mutation operators [7] they have been shown to result in close to minimal distance TSP tour lengths. These systems have relied on the use of fully ....

[Article contains additional citation context not shown here]

D.B. Fogel, Applying Evolutionary Programming to Selected Travelling Salesman Problems, Cybernetics and Systems: An Int. Journal, 1993, 24:27-36.

....is to find a shorted closed tour through a given set of n cities with known inter city distances such that each city is visited exactly once and the tour ends at the start city. The TSP has played a central role in the development of many nature inspired heuristics like Evolutionary Computation [6, 7, 29, 30], Ant Colony Optimization [4, 26] Neural Networks [1, 10] or Simulated Annealing [14, 15] to name just the most important ones. Currently, for several such approaches very good performance is reported. Examples are the genetic algorithms (GAs) by Merz and Freisleben [7, 19] Walters [29] Nagata ....

D. B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 24:27--36, 1993.

....based on EAs can self select training examples. Cho and Cha [289] proposed another algorithm for evolving training sets by adding virtual samples. 5. 2 Artificial Neural Network as Fitness Estimator Evolutionary algorithms have been used with success to optimize various control parameters [290, 291, 292]. However, it is very time consuming and costly to obtain fitness values for some control problems as it is impractical to run a real system for each combination of control parameters. In order to get around this problem and make evolution more efficient, fitness values are often approximated ....

D. B. Fogel, "Applying evolutionary programming to selected control problems," Computers and Mathematics with Applications, vol. 27, no. 11, pp. 89--104, 1994.

No context found.

D.B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 63:111--114, 1993.

No context found.

D.B. Fogel. Applying evolutionary programming to selected travelling salesman problems. Cybernetics and Systems, 63:111--114, 1993.

No context found.

D. B. Fogel, "Applying Evolutionary Programming to Selected Traveling Salesman Problems," Cybernetics and Systems, vol. 24, pp. 27--36, 1993.

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D. B. Fogel, \Applying Evolutionary Programming to Selected Traveling Salesman Problems," Cybernetics and Systems, 24, (1993), 27-36.

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D. B. Fogel, "Applying evolutionary programming to selected traveling salesman problems," Cybernetics and Systems, 24:27--36, 1993.

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Fogel, D. B. (1993) "Applying evolutionary programming to selected traveling salesman problems," Cybernetics and Systems, vol.24, pp.27--36.

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D. B. Fogel, "Applying evolutionary programming to selected traveling salesman problems," Cybernetics and Systems, vol. 24, pp. 27--36, 1993.

No context found.

D.B. Fogel. Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems, 24(1):27--36, 1993.

No context found.

D. B. Fogel, "Applying evolutionary programming to selected control problems," Comput. Math. Applicat., vol. 27, no. 11, pp. 89--104, 1994.

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Fogel DB. Applying evolutionary programming to selected traveling salesman problems Cybernetics and Systems, ,1993, 24(1):27-36.

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Fogel DB. Applying evolutionary programming to selected traveling salesman problems Cybernetics and Systems, ,1993, 24(1):27-36.

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Fogel, D.B. (1993). Applying Evolutionary Programming to Selected Traveling Salesman Problems, Cybernetics and Systems, 24, pp. 27-36.

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