P. Jog, J. Y. Suh, and D. V. Gucht, "Parallel Genetic Algorithms Applied to the Traveling Salesman Problem," SIAM Journal on Optimization, vol. 1, no. 4, pp. 515-- 529, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....them. As the word genetic algorithm (GA) is also used to mean the general framework including GLS, we use simple GA to denote the genetic algorithms which do not incorporate LS, if we want to distinguish them from GLS. The basic idea of GLS was proposed in [18] Early references such as [34, 68, 82, 83, 111, 113, 114, 146, 151] have also mentioned the idea of GLS. To our knowledge, the word genetic local search rst appeared in [151] A similar but more general mechanism of generating initial solutions from many reference solutions is also proposed and is called the scatter search [53, 56, 57] While good solutions ....

P. Jog, J.Y. Suh and D. van Gucht, \Parallel genetic algorithms applied to the traveling salesman problem," SIAM J. Optimization, vol.1, pp.515-529, 1991.

....in each subpopulation is broadcast to all the other subpopulations, and (2) among the neighboring nodes, i.e. only the neighboring subpopulations receive the best strings. The most important aspects of PGAs, which result in a considerable speedup relative to sequential GAs, are the following [58]: Local selection, i.e. a selection of an individual in a neighborhood is introduced, in contrast with the selection in original GAs which is performed by considering the whole population. Asynchronous behavior which allows the evolution of di#erent population structures at di#erent ....

....evolution of di#erent population structures at di#erent speeds, possibly resulting in an overall improvement of the algorithm in terms of CPU time. Reliability in computation performance, i.e. the performance of one processor does not a#ect the performance of the other processors. Jog et al. [58] consider two basic categories of parallel genetic algorithms: 1. Coarse grained PGAs, where subpopulations are allocated to each processor of the parallel machine. The selection and recombination steps are performed within a subpopulation. 2. Fine grained PGAs, where a single individual or a ....

P. Jog, J.Y. Suh and D. Van Gucht, Parallel Genetic Algorithms Applied to the Traveling Salesman Problem, SIAM Journal of Optimization, 1 (1991), pp.515--529.

.... Modelling, 25] Memoirs of the Faculty of Engineering, Fukui University, 156] Memoirs of the Faculty of Engineering, Okayama University, 37] Neural Computation, 145] OR Spektrum, 133] Parallel Processing Letters, 114] Physical Review Letters, 92, 98] SIAM Journal on Optimization, [154] Systems Analysis Modeling Simulation, 106] Trans. Soc. Instrum. Control Eng. Japan) 88] Transactions of the Institute of Electronics, Information, and Communication Engineers D I, 85] Wirtschaftsinformatik, 131] Wuhan Univ. J. Nat. Sci. China) 64] total 56 articles in 45 series ....

....John G. 117] Garigliano, Roberto, 141] Gause, Donald C. 21] Gen, Mitsuo, 23] Gold, S onke Sonnich, 150] Goldberg, David E. 123] Gopal, Rajeev, 124] Gorges Schleuter, Martina, 71] Graham, Paul, 35] Grefenstette, John J. 124] Guan, Shanguchuan, 125, 126, 136] Gucht, Dirk Van, [124, 128, 154] Guertin, Fran cois, 62] Gus eld, D. 65] Han, Seung Kee, 47] Haneda, H. 84] Hilliard, M. R. 135] Holland, J. R. C. 127] Homaifar, Abdollah, 125, 126, 136] Hoos, H. 78] Horng, Jorng Tzong, 45] Houdayer, J. 92, 98] Hsu, Chin Chih, 49, 83] Hsu, Ching Chi, 116] Ikeda, Y. 156] ....

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Prasanna Jog, Jung Y. Suh, and Dirk Van Gucht. Parallel genetic algorithms applied to the traveling salesman problem. SIAM Journal on Optimization, 1(4):515-529, ? 1991. yLevine93a ga:Suh91a.

....were inspired by the biological process of evolution. They were introduced by Holland (1975) and popularized by Goldberg (1989) for optimization of continuous functions. More recently they have been used with success on many classical NP hard combinatorial problems such as the traveling salesman (Jog et al. 1991), redundancy allocation (Coit et al. 1996) and job shop scheduling (Storer et al. 1992) The main distinguishing characteristics of GA are a set (population) of candidate solutions, a breeding mechanism to create new solutions (children) by recombination (crossover) of existing (parent) ....

Jog, P., J. Y. Suh and D. Van Gucht, "Parallel genetic algorithms applied to the traveling salesman problem," SIAM Journal of Optimization, 1991 51, 515-529.

....of multi modal functions. Goldberg [14, 15] expanded the theoretical foundations of GA, as well as the range of applications. GA methods have been successfully extended to classical combinatorial optimization problems, including job shop scheduling [37] the Traveling Salesman Problem (TSP) [16, 22, 42], VLSI component placement [7] quadratic assignment problems [21, 38] and others. This paper discusses previous approaches using GA search for constrained optimization problems, then introduces the general adaptive penalty approach. The adaptive penalty is demonstrated to be both effective and ....

P. Jog, J. Y. Suh and D. Van Gucht, 1991, Parallel Genetic Algorithms Applied to the Traveling Salesman Problem, SIAM Journal of Optimization 51, 515-529.

....fitness function to penalize strings that violate constraints. The idea is to degrade the fitness of infeasible strings but not throw away valuable information contained in the cost term of the fitness function. Below we discuss some examples of the second and third approaches. Jog, Suh, and Gucht [39] summarize many of the crossover operators used for the traveling salesperson problem (TSP) In general, these operators try to include as much of the parent strings as possible in the offspring, subject to the constraint that the offspring contain a valid tour. In the TSP, since all cities are ....

....speed the sequential GA at each node by using the multiple processors to parallelize the generation loop. Parallel genetic algorithms can be classified according to the granularity of the distributed population, coarse grained vs. fine grained, and the manner in which the GA operators are applied [39]. In a coarse grained PGA the population is divided into several subpopulations, each of which runs a traditional GA independently and in parallel on its own subpopulation. Occasionally, fit strings migrate from one subpopulation to another. In some implementations migrant strings may move only to ....

[Article contains additional citation context not shown here]

P. Jog, J. Suh, and D. Gucht. Parallel Genetic Algorithms Applied to the Traveling Salesman Problem. Technical Report No. 314, Indiana University, 1990.

....exist. Recent work in this regard includes [11, 12, 10] On the empirical side, a large body of work exists in applying genetic algorithms to different application areas, as well as experimental evaluation on hard combinatorial optimization problems, notably the traveling salesman problem [9, 4, 13, 8]. Although the jury is still out, there is general agreement that genetic algorithms need to be tailored to the particular application at hand, and in the case of the traveling salesman problem, genetic algorithms have been exhibited which approach, and in some cases exceed, the performance of ....

P. Jog, J. Suh, and D. Gucht. Parallel Genetic Algorithms Applied to the Traveling Salesman Problem. SIAM Journal on Optimization, 1(4):515-529, 1991.

.... y Supported in part by NSF grant CCR 9204284 1 Introduction Genetic algorithms [8] viewed as general purpose optimization procedures, are increasingly being applied to a diverse spectrum of problem areas, ranging from protein folding to crew scheduling in the airline industry, to name a few [3, 5, 6, 9, 11, 12, 15]. Although research abounds, the jury is still out with respect to the utility of genetic search as a pure optimization technique. In part, this is due to the nonuniformity of problem instances which makes comparing results across different domains difficult. In other cases, the problem instances ....

P. Jog, J. Suh, and D. Gucht. Parallel Genetic Algorithms Applied to the Traveling Salesman Problem. SIAM Journal on Optimization, 1(4):515--529, 1991.

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P. Jog, J. Y. Suh, and D. V. Gucht, "Parallel Genetic Algorithms Applied to the Traveling Salesman Problem," SIAM Journal on Optimization, vol. 1, no. 4, pp. 515-- 529, 1991.

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P. Jog, J. Y. Suh, and D. V. Gucht, \Parallel Genetic Algorithms Applied to the Traveling Salesman Problem," SIAM Journal on Optimization, 1 (4), (1991), 515-529.

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P. Jog, J.Y. Suh, and D.V. Gucht. Parallel Genetic Algorithms Applied to the Traveling Salesman Problem. SIAM Journal of Optimization, 1(4):515--529, 1991.

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