Abstract
In this paper, an algorithm to determine the most probable model, amongst a large number of models formed with a set of wider class of basis functions, based on Bayesian model comparison is developed. The models consist of linear coefficients and nonlinear basis functions, which may themselves be parametrised, with different models constructed with different subsets of basis functions. By a suitable encoding, genetic algorithms are used to search over the space of all possible subsets of basis functions to determine the most probable model that describes the given observations.
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