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Huang, D., Lin, J.
Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems using MapReduce

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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