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We present GADRAD, a Python module that adopts heuristic search techniques inthe form of genetic algorithms, to efficiently model post-asymptotic giantbranch (post- AGB) disc environments. GADRAD systematically constructs themulti-dimensional pa- parameter probability density functions that arise fromthe fitting of radiative transfer and geometric models to opticalinterferometric data products. The result provides unbiased descriptions of theobject's potential morphology, component luminosities and temperatures, dustcomposition, disc density profiles and mass. Correlation in the estimatedparameters as well as potential degeneracies are revealed. Estimatedprobability distributions of the post-AGB environment parameters provideinsight into the shaping processes that may occur in the transition from thepost-AGB to the planetary nebula phase. We test parameter recovery on simulatedartificial data products of a typical post-AGB environment. We then use GADRADto model the mid-infrared spectrum and visibilities of the Ant nebula (Mz3),taken with the Very Large Telescope Interferometer's instrument MIDI. Ourresult is consistent with a large dusty disc with similar parameter values tothose previously found by Chesneau et al., except for a larger dust mass of$3.5^{+7.5}_{-2.2}\times10^{-5}$ M$_{\odot}$. The parameter confidenceintervals determined by GADRAD, can however be relied upon to impose additionalconstraints on all disc and system parameters. Based on our analysis and otherconsiderations, we tentatively suggest that Mz3 is a pre-PN ejected during amagnetic (polar) common envelope interaction, where the binary may or may nothave survived at the core of the nebula.

A genetic algorithm approach to fitting interferometric data of post-AGB objects: I. the case of the Ant nebula