Inspired by Darwinian evolution, a genetic algorithm (GA)
approach is one of the popular heuristic methods for solving
hard problems, such as the Job Shop Scheduling Problem
(JSSP), which is one of the hardest problems where there
lacks efficient exact solutions. It is intuitive that the population
size of a GA may greatly affect the quality of the
solution, but it is unclear how a large population helps in
finding good solutions. In this paper, a GA is implemented
to scale the population using MapReduce, a framework running
on a cluster of computers that aims to provide largescale
data processing. The experiments are conducted on a
cluster of 414 machines, and population sizes up to 107 are
inspected. It is shown that larger population sizes not only
tend to find better solutions, but also require fewer generations.
It is clear that when dealing with a hard problem like
JSSP, an existing GA can be improved by scaling up populations,
whereby MapReduce can handle massive populations
efficiently within reasonable time.
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