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首页> 外文会议>2019 IEEE 5th Conference on Knowledge Based Engineering and Innovation >Training Feed-forward Neural Networks using Asexual Reproduction Optimization (ARO) Algorithm
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Training Feed-forward Neural Networks using Asexual Reproduction Optimization (ARO) Algorithm

机译：使用无性繁殖优化（ARO）算法训练前馈神经网络

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Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identifying the weight of neurons which is one of the main issues addressed in this field. The process of network learning by back-propagation algorithm which is based on gradient, commonly fall into a local optimum. Due to the importance of weights and neural network structure, evolutionary neural networks have been emerged to obtain suitable weight set. This paper will concentrate on training a feed-forward networks by a modified evolutionary algorithm based on asexual reproduction optimization (ARO) in order to data classification problems. The idea is to use real representation (rather the binary) for adjusting weights of the network. Experimental results show a better result in terms of speed and accuracy compared with other evolutionary algorithms including genetic algorithms, simulated annealing and particle swarm optimization.
机译：人工神经网络由于其学习能力，鲁棒性和可扩展性而越来越多地用于许多数据分类问题。通过识别神经元的重量来完成神经网络的训练，这是该领域解决的主要问题之一。通过基于梯度的反向传播算法进行网络学习的过程通常属于局部最优。由于权重和神经网络结构的重要性，已经出现了进化神经网络来获得合适的权重集。本文将致力于通过基于无性繁殖优化（ARO）的改进进化算法来训练前馈网络，以解决数据分类问题。这个想法是使用实​​数表示（而不是二进制）来调整网络的权重。实验结果表明，与其他进化算法（包括遗传算法，模拟退火算法和粒子群优化算法）相比，该算法在速度和准确性上都有更好的结果。

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