Selecting Initial States from Genetic Tempering for Efficient Monte Carlo Sampling

Abstract

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 practice. Models tested here (Ising, anti-ferromagnetic Kagome, and random-bond Ising) are sampled on a time scale of seconds and with a small uncertainty that is free from auto-correlation.

arXiv comment4 pages, 4 figures

Cited by in corpus (3)

• Electronic and Magnetic Properties of the Candidate Magnetocaloric-Material Double Perovskites La${}_2$MnCoO${}_6$, La${}_2$MnNiO${}_6$ and La${}_2$MnFeO${}_6$
• Electronic and Magnetic Properties of double-perovskites La${}_2$MnRuO${}_6$ and Hole-Doped La${}_2$MnFeO${}_6$ and their Potential for Magnetic Refrigeration
• A theory of the phenomenology of multipopulation genetic algorithm with an application to the Ising model