基于最佳生存策略的并行遗传算法训练前馈神经网络

Ali Kattan, R. Abdullah, R. A. Salam
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引用次数: 15

摘要

前馈人工神经网络(FFANN)可以使用遗传算法(GA)进行训练。遗传算法提供了一种随机全局优化技术,但可能存在两个主要缺点:收敛时间慢和数据表示不切实际。在数据集更大的FFANN中,这些缺点的影响更大。使用非二进制实数编码数据表示,我们对用于FFANN训练的分代遗传算法进行了改进。这种增强可能有两个方面:第一个是一种新的策略,通过允许最适合的字符串根据其年龄在下一个种群中不变地存活下来,来处理种群的字符串。二是通过利用已知的矩阵乘法并行处理技术来加快适应度计算时间。在通过以太网连接的商用计算机的主从架构上进行实现。使用一个著名的基准测试数据集,结果表明我们提出的技术在总体收敛时间和处理时间方面都优于标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training Feed-Forward Neural Networks Using a Parallel Genetic Algorithm with the Best Must Survive Strategy
Feed-Forward Artificial Neural Networks (FFANN) can be trained using Genetic Algorithm (GA). GA offers a stochastic global optimization technique that might suffer from two major shortcomings: slow convergence time and impractical data representation. The effect of these shortcomings is more considerable in case of larger FFANN with larger dataset. Using a non-binary real-coded data representation we offer an enhancement to the generational GA used for the training of FFANN. Such enhancement would come in two fold: The first being a new strategy to process the strings of the population by allowing the fittest string to survive unchanged to the next population depending on its age. The second is to speed up fitness computation time through the utilization of known parallel processing techniques used for matrix multiplication. The implementation was carried on master-slaves architecture of commodity computers connected via Ethernet. Using a well-known benchmarking dataset, results show that our proposed technique is superior to the standard in terms of both the overall convergence time and processing time.
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