Combining Artificial Algae Algorithm to Artificial Neural Network for Optimization of Weights

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Gülay Tezel
Sait Ali Uymaz
Esra Yel


Artificial Neural Network (ANN) is one of the most important artificial intelligent algorithms used for classification problems. The structure of ANN depends on the learning algorithm used for adjusting the weights between neurons of the layers according to the calculated error between model value and the real value. Recently the weights between layers in ANN has been optimized by using metaheuristic optimization algorithms. One of the recent high performance nonlinear optimization algorithms is Artificial Algae Algorithm (AAA) which is a bioinspired, successful, competitive and robust optimization algorithm. In this study, AAA was used as a tool for optimization of the weights in ANN algorithm. ANN and AAA was combined such that the training step of the ANN modeling to be performed by AAA. After training, ANN continues testing with the optimized weights. The established model combination (AAANN) was tested on three benchmarked datasets (Iris, Thyroid and Dermatology) of the UCI Machine Learning Repository to indicate the performance of this hybrid structure. The results were compared with MLP algorithm in terms of Mean Absolute Error (MAE). Accordingly, up to 96% reduction in mean MSE levels could be achieved by AAANN for all models.

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G. Tezel, S. Uymaz, and E. Yel, “Combining Artificial Algae Algorithm to Artificial Neural Network for Optimization of Weights”, DataSCI, vol. 1, no. 1, pp. 37-44, Dec. 2018.
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