  Stabilizing the complexity of Feedforward Neural Networks (FNNs) for the
given approximation task can be managed by defining an appropriate model
magnitude which is also greatly correlated with the generalization quality and
computational efficiency. However, deciding on the right level of model
complexity can be highly challenging in FNN applications. In this paper, a new
Model Selection algorithm using Binary Ant Colony Optimization (MS-BACO) is
proposed in order to achieve the optimal FNN model in terms of neural
complexity and cross-entropy error. MS-BACO is a meta-heuristic algorithm that
treats the problem as a combinatorial optimization problem. By quantifying both
the amount of correlation exists among hidden neurons and the sensitivity of
the FNN output to the hidden neurons using a sample-based sensitivity analysis
method called, extended Fourier amplitude sensitivity test, the algorithm
mostly tends to select the FNN model containing hidden neurons with most
distinct hyperplanes and high contribution percentage. Performance of the
proposed algorithm with three different designs of heuristic information is
investigated. Comparison of the findings verifies that the newly introduced
algorithm is able to provide more compact and accurate FNN model.
