Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
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RESEARCH ARTICLE-COMPUTER ENGINEERING AND COMPUTER SCIENCE
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm Ahmet Cevahir Cinar1 Received: 28 March 2020 / Accepted: 13 August 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract The artificial neural network (ANN) is the most popular research area in neural computing. A multi-layer perceptron (MLP) is an ANN that has hidden layers. Feed-forward (FF) ANN is used for classification and regression commonly. Training of FF MLP ANN is performed by backpropagation (BP) algorithm generally. The main disadvantage of BP is trapping into local minima. Nature-inspired optimizers have some mechanisms escaping from the local minima. Tree-seed algorithm (TSA) is an effective population-based swarm intelligence algorithm. TSA mimics the relationship between trees and their seeds. The exploration and exploitation are controlled by search tendency which is a peculiar parameter of TSA. In this work, we train FF MLP ANN for the first time. TSA is compared with particle swarm optimization, gray wolf optimizer, genetic algorithm, ant colony optimization, evolution strategy, population-based incremental learning, artificial bee colony, and biogeography-based optimization. The experimental results show that TSA is the best in terms of mean classification rates and outperformed the opponents on 18 problems. Keywords Tree-seed algorithm · Multi-layer perceptron · Training neural network · Artificial neural network · Neural networks · Nature inspired algorithms
1 Introduction Neural computing mimics the human brain which is the most complex organ of the human body [1]. Neural networks simulate the connections in the human brain [2]. This simulation is named as the artificial neural network (ANN). Basically ANN takes inputs, computes them, and produces the outputs. This process is a learning process. Learning has two main types: supervised and unsupervised. In supervised learning, the training data have output labels but in unsupervised learning, the data have not got output labels. ANN is a balancer between the input and outputs. In the literature, there are various types of networks such as feedforward (FF) [3], Kohonen [4], radial-basis function (RBF) [5], recurrent neural [6], spiking neural [7]. FF is a network that has one way (one direction). In FF, the association of inputs and outputs is provided with weights and biases. If a FF ANN has hidden layers, it is named as multi-layer percep-
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Ahmet Cevahir Cinar [email protected]; [email protected] Department of Computer Engineering, Faculty of Technology, Selcuk University, 42075 Konya, Turkey
tron (MLP) [8, 9]. MLP has three layers: input, hidden, and output. Training data are used for learning the hidden weights between the attributes and class labels. The deterministic and stochastic learning approaches are used for training an ANN. The gradient-based methods and backpropagation (BP) algorithm are deterministic methods [10]. If the training data does
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