Parallel genetic algorithm for a hypercube  [1987]

Tanese, R.

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This paper discusses a parallel genetic algorithm for a medium-grained hypercube computer. Each processor runs the genetic algorithm on its own sub-population, periodically selecting the best individuals from the sub-population and sending copies of them to one of its neighboring processors. The performance of the parallel algorithm on a function maximization problem is compared to the performance of the serial version. The parallel algorithm achieves comparable results with near-linear speed-up. In addition, some experiments were performed to study the effects of varying the parameters for the parallel model.

Other subjects

  • parallel algorithms
  • algorithms
  • computer analysis
  • computer software
  • genetic models
  • parallel computing
  • computers

From the journal

Genetic algorithms and their applications : proceedings of the second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA