Training multi-layer perceptron with artificial algae algorithm
Bahaeddin Türkoğlu, Ersin Kaya
Abstract
Artificial Neural Networks are commonly used to solve problems in many areas, such as classification, pattern recognition, and image processing. The most challenging and critical phase of an Artificial Neural Networks is related with its training process. The main challenge in the training process is finding optimal network parameters (i.e. weight and biase). For this purpose, numerous heuristic algorithms have been used. One of them is Artificial Algae Algorithm, which has a nature-inspired metaheuristic optimization algorithm. This algorithm is capable of successfully solving a wide variety of numerical optimization problems. In this study, Artificial Algae Algorithm is proposed for training Artificial Neural Network. Ten classification datasets with different degrees of difficulty from the UCI database repository were used to compare the proposed method performance with six well known swarm-based optimization and backpropagation algorithms. The results of the study show that Artificial Algae Algorithm is a reliable approach for training Artificial Neural Networks.