Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-04-10
Computer Science
Neural and Evolutionary Computing
8 pages, 7 figures, accepted to the Genetic and Evolutionary Computation Conference (GECCO 2007)
Scientific paper
We present a genetic algorithm which is distributed in two novel ways: along genotype and temporal axes. Our algorithm first distributes, for every member of the population, a subset of the genotype to each network node, rather than a subset of the population to each. This genotype distribution is shown to offer a significant gain in running time. Then, for efficient use of the computational resources in the network, our algorithm divides the candidate solutions into pipelined sets and thus the distribution is in the temporal domain, rather that in the spatial domain. This temporal distribution may lead to temporal inconsistency in selection and replacement, however our experiments yield better efficiency in terms of the time to convergence without incurring significant penalties.
Aggarwal Varun
Kim Minkyu
Medard Muriel
O'Reilly Una-May
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