Corcoran, A. L. and Wainwright, R. L. (1994). A parallel island model genetic algorithm for the multiprocessor scheduling problem. In Selected Areas in Cryptography, pages 483--487.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....determined by dividing the total population size by the number of processors used, as usually found in a conventional coarse grained model. There is a related work which reported the comparison between the distributed population and the fixed population on each processor, called scaled parallel GA (Corcoran, 1994). 3 PARALLEL GENETIC ALGORITHM In the first stage of the implementation, the population size was set at 400, as was used in the previous work (Chongstitvatana, 1999) The execution time is reduced due to the probabilistic advantage in the larger population size. In the second stage of the ....

A. L. Corcoran, and R. L. Wainwright (1994). A parallel island model genetic algorithm for the multiprocessor scheduling problem. In Proceedings of the 1994 ACM/SIGAPP Symposium on Applied Computing, 483-487.

....availability of free software (like MPI and PVM) 10] and its ease of implementation. Other work on applying PGA to timetable problems [2] uses MasterSlave parallelization method implemented on a commercial shared memory multiprocessor. However Master Slave parallelization is machine dependent [12]. A shared memory multiprocessor computer is not easily available when compared with a clustered computer, which consists of network of workstations. This paper is organized as follows: The next section is a problem formulation and Section 3 describes a problem representation in GA. Section 4 ....

A. L. Corcoran and R. L. Wainwright, "A Parallel Island Model Genetic Algorithm for the Multiprocessor Scheduling Problem", Proceedings of the 1994 ACM/SIGAPP Symposium on Applied Computing, ACM Press, March 6-8, 1994, pp. 483-487.

....This perhaps in some sense better , at least it shows improvement as the number of populations increase. Our goal in this paper is not a thorough analysis of the fitness performance of the island model. The island s model ability to outperform the serial GA has been established in other papers [2] [9] Our main concern is why the performance degrades so noticeably as we add machines and populations (figure 1) The slave s code included a blocking receive in which it waited for a command from the master. Essentially all slave tasks were being synchronized after each epoch. After all slaves ....

A. Corcoran and R. Wainwright. A Parallel Island Model Genetic Algorithm for the Multiprocessor Scheduling Problem. Technical Report, University of Tulsa.

....meshes an the system itself is a square mesh of size a power of two. For other related work see [6, 15, 16, 24, 36, 41] The LibGA implementation for the multiprocessor scheduling problem is shown below. This is an extremely simplified version of the one the authors studied in more detail in [12]. Figure 17, Figure 18, and Figure 19 show the GA for multiprocessor scheduling, gams.c. As before, the GA is configured with GA config( the set of tasks is read from the data file, and the chromosome length is set to the number of tasks. The objective function in Figure 18 evaluates the total ....

A. L. Corcoran and R. L. Wainwright. A parallel island model genetic algorithm for the multiprocessor scheduling problem. In E. Deaton, K. M. George, H. Berghel, and G. Hedrick, editors, Proceedings of the

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Corcoran, A. L. and Wainwright, R. L. (1994). A parallel island model genetic algorithm for the multiprocessor scheduling problem. In Selected Areas in Cryptography, pages 483--487.

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