A. L. Corcoran and R. L. Wainwright. Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems, volume 1 of Lance Chambers, Editor, Practical Handbook of Genetic Algorithms, Applications, pages 143--172. CRC Press, 1995.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of the algorithm in each node 4 Experiment and Discussion 4.1 Experimental Design The experiments are carried out on a dedicated cluster of PC workstations. The number of processing nodes used in the experiments is 8. The program is based on a modified version of LibGA software package [10]. MPICH, a portable implementation of MPI standard, is used for providing communication functions in parallel computing environment. The following algorithms using the proposed method and other methods are examined and compared. 1. Adaptive algorithm: This is the proposed method. 2. Uniform ....

A. L. Corcoran and R. L. Wainwright. Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems, volume 1 of Lance Chambers, Editor, Practical Handbook of Genetic Algorithms, Applications, pages 143--172. CRC Press, 1995.

....uneven work loads among processors. 4. EXPERIMENT AND DISCUSSION 4.1. Experimental Design The experiments are carried out on a dedicated cluster of PC workstations. The number of processing nodes used in the experiments is 8. The program is based on a modified version of LibGA software package [14]. MPICH, a portable implementation of MPI standard, is used for providing communication functions in parallel computing environment. The following algorithms using the proposed method and other methods are examined and compared. 1. Adaptive algorithm: This is the proposed method. 2. Uniform ....

A. L. Corcoran and R. L. Wainwright. Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems, volume 1 of Practical Handbook of Genetic Algorithms, L. Chambers (ed.), chapter 6, pages 143--172. CRC Press, 1995.

....node degree distribution and larger cliques than the usual random graphs [29] the MANN graphs are generated for the set covering problem (cf. 15] and the Cx.y and DISJx.y graphs are random graphs of size x and density 2 y. The HGA algorithm has been implemented in C using the LibGa system [8], with slight code modifications in order to include the diversification procedure into the selection mechanism, and the HA procedure into the objective function. No attempt was made to optimize the code for this specific problem. The GA parameters are shown in Figure 2. The initial population has ....

A.L. Corcoran and R.L. Wainwright. Using LibGA to develop genetic algorithms for solving combinatorial optimization problems. In Lance Chambers, editor, The Application Handbook of Genetic Algorithms, pages 143--172. CRC Press, 1995.

....mechanisms to attach either higher or lesser priority to each of the conflicting objectives. GENETIC ALGORITHMS Several researchers have investigated the benefits of solving combinatorial problems using genetic algorithms. Davis [5, 6] Goldberg [7] Rawlins [10] and Corcoran and Wainwright [3] provide an excellent indepth study of genetic algorithms. The genetic algorithm operates on a fixed size population of chromosomes. In the context of combinatorial optimization, a chromosome is a string of genes that represents an encoding of a candidate solution. An allele is a value assumed by ....

....The genes of a chromosome may be bits, integers, floating point numbers, or instances of some other primitive data type. This research work uses an order based genetic algorithm where a chromosome is a permutation of integers. The genetic algorithm implementation used in this research is LibGA [3]. GENETIC ALGORITHM ENCODING As stated previously, the ITP for a graph G is NP hard. Noting that step 1 and step 3 in the VCNN are each NPcomplete, one strategy for using a genetic algorithm to find near optimal solutions to the ITP problem is to apply the GA technique to determine a near optimal ....

A. Corcoran and R. Wainwright, "Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems," in Practical Handbook of Genetic Algorithms, Applications, Volume 1, Editor Lance Chambers, CRC, 1995.

....algorithm where a chromosome A B E H C D G F (3,8) 4,8) 7,8) 3,1) 1,6) 6,6) 7,5) 3,1) 5,4) 8,4) 9,6) Figure 2: Communications Network after Routing R2, R3, and R4 using PMRP is a permutation of integers. The genetic algorithm implementation used in this research is LibGA [3]. It is assumed the reader is generally familiar with the fundamentals of genetic algorithms. STEINER TREE PROBLEM IN A GRAPH The Steiner Problem in a Graph (SPG) is a classic combinatorial optimization problem. The SPG has been shown to be NP complete. Given a graph and a required subset of ....

A.L. Corcoran and R.L. Wainwright, "Using LibGA to Develop Genetic Algorithms for Solving Combinatorial Optimization Problems", Practical Handbook of Genetic Algorithms, Vol. 1, Lance Chambers, ed., CRC Press, 1995, pp 143- 172.

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