| Summary: | Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). One
of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires
a rather large amount of available measures in order to achieve good classification accuracy. To
overcome this, a Functional Link Neural Networks (FLNN), which has single layer of trainable
connection weight is used. The standard method for tuning the weight in FLNN is using a
Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as
trapping in local optima and slow convergence especially for solving non-linearly separable
classification problems. In this paper, a modified Artificial Bee Colony (mABC) is used to recover the
BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of
the mABC as a learning scheme for FLNN has given a better accuracy result for the classification
tasks.
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