A simple but massively parallel Monte Carlo method is demonstrated here. Working with many different Monte Carlo samplers creates the opportunity to arrange the systems to partially cancel errors from insufficient relaxation. By averaging independent runs, auto-correlation is automatically canceled. This arrangement represents the idealized limit of parallel tempering. In order to determine an appropriate initial distribution, un-relaxed samples are randomly selected. Results from this method, called Genetic Tempering, for a variety of spin models are presented.This project was funded solely by Institut quantique. This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund (CFREF).
APS March Meeting Abstracts
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