  Optimization of neural network (NN) significantly influenced by the transfer
function used in its active nodes. It has been observed that the homogeneity in
the activation nodes does not provide the best solution. Therefore, the
customizable transfer functions whose underlying parameters are subjected to
optimization were used to provide heterogeneity to NN. For the experimental
purpose, a meta-heuristic framework using a combined genotype representation of
connection weights and transfer function parameter was used. The performance of
adaptive Logistic, Tangent-hyperbolic, Gaussian and Beta functions were
analyzed. In present research work, concise comparisons between different
transfer function and between the NN optimization algorithms are presented. The
comprehensive analysis of the results obtained over the benchmark dataset
suggests that the Artificial Bee Colony with adaptive transfer function
provides the best results in terms of classification accuracy over the particle
swarm optimization and differential evolution.
