  We recently presented a new "artificial intelligence" method for the analysis
of high-resolution absorption spectra (Bainbridge and Webb, Mon. Not. R.
Astron. Soc. 2017, 468,1639-1670). This new method unifies three established
numerical methods: a genetic algorithm (GVPFIT); non-linear least-squares
optimisation with parameter constraints (VPFIT); and Bayesian Model Averaging
(BMA). In this work, we investigate the performance of GVPFIT and BMA over a
broad range of velocity structures using synthetic spectra. We found that this
new method recovers the velocity structures of the absorption systems and
accurately estimates variation in the fine structure constant. Studies such as
this one are required to evaluate this new method before it can be applied to
the analysis of large sets of absorption spectra. This is the first time that a
sample of synthetic spectra has been utilised to investigate the analysis of
absorption spectra. Probing the variation of nature's fundamental constants
(such as the fine structure constant), through the analysis of absorption
spectra, is one of the most direct ways of testing the universality of physical
laws. This "artificial intelligence" method provides a way to avoid the main
limiting factor, i.e., human interaction, in the analysis of absorption
spectra.
