• Corpus ID: 67816953

Selecting Initial States from Genetic Tempering for Efficient Monte Carlo Sampling

@article{Baker2018SelectingIS,
  title={Selecting Initial States from Genetic Tempering for Efficient Monte Carlo Sampling},
  author={Thomas E. Baker},
  journal={arXiv: Statistical Mechanics},
  year={2018}
}
  • T. E. Baker
  • Published 29 January 2018
  • Computer Science
  • arXiv: Statistical Mechanics
An alternative to Monte Carlo techniques requiring large sampling times is presented here. Ideas from a genetic algorithm are used to select the best initial states from many independent, parallel Metropolis-Hastings iterations that are run on a single graphics processing unit. This algorithm represents the idealized limit of the parallel tempering method and, if the threads are selected perfectly, this algorithm converges without any Monte Carlo iterations--although some are required in… 

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