Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks.
Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks.
复制标题
语法进化优化神经网络的替代交叉策略和选择技术。
DOI：
10.1145/1143997.1144163
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Ritchie,MarylynD
中科院分区：
文献类型：
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作者：
Motsinger,AlisonA;Hahn,LanceW;Dudek,ScottM;Ryckman,KelliK;Ritchie,MarylynD
人类遗传学中最困难的挑战之一是识别和表征常见复杂人类疾病的易感基因。基因-基因和基因-环境相互作用的存在构成了这些疾病的遗传结构，这提出了一个重大的统计挑战。随着该领域向具有数十万甚至数百万变量的全基因组关联研究的方向发展，新的统计和计算方法的发展是必要的。在此之前，我们介绍了一种语法进化优化NN（GENN），以改进为纯前馈反向传播神经网络选择最佳架构的试错过程。GENN优化了来自大量变量池的输入、权重和网络的连通性，包括隐藏层的数量和隐藏层中的节点数量。因此，该算法自动生成给定数据集的最佳神经网络架构。像所有的进化计算算法一样，语法进化依赖于交叉和选择等进化算子来学习给定数据集的最佳解决方案。我们想了解适应度比例与顺序选择方案的效果，以及标准和新的交叉策略对GENN性能的影响。
One of the most difficult challenges in human genetics is the identification and characterization of susceptibility genes for common complex human diseases. The presence of gene-gene and gene-environment interactions comprising the genetic architecture of these diseases presents a substantial statistical challenge. As the field pushes toward genome-wide association studies with hundreds of thousands, or even millions, of variables, the development of novel statistical and computational methods is a necessity. Previously, we introduced a grammatical evolution optimized NN (GENN) to improve upon the trial-and-error process of choosing an optimal architecture for a pure feed-forward back propagation neural network. GENN optimizes the inputs from a large pool of variables, the weights, and the connectivity of the networkincluding the number of hidden layers and the number of nodes in the hidden layer. Thus, the algorithm automatically generates optimal neural network architecture for a given data set. Like all evolutionary computing algorithms, grammatical evolution relies on evolutionary operators like crossover and selection to learn the best solution for a given dataset. We wanted to understand the effect of fitness proportionate versus ordinal selection schemes, and the effect of standard and novel crossover strategies on the performance of GENN.