This paper proposes a novel approach to improve the training efficiency and the generalization performance of Feed Forward Neural Networks (FFNNs) resorting to an optimal rescaling of input features (OFR) carried out by a Genetic Algorithm (GA). The OFR reshapes the input space improving the conditioning of the gradient-based algorithm used for the training. Moreover, the scale factors exploration entailed by GA trials and selection corresponds to different initialization of the first layer weights at each training attempt, thus realizing a multi-start global search algorithm (even though restrained to few weights only) which fosters the achievement of a global minimum. The approach has been tested on a FFNN modeling the outcome of a real industrial process (centerless grinding).

翻译：本文提出了一种新颖的方法，通过遗传算法（GA）对输入特征进行最优重缩放（OFR），以提高前馈神经网络（FFNNs）的训练效率和泛化性能。OFR重塑输入空间，改善了用于训练的基于梯度算法的条件性。此外，GA试验和选择所涉及的尺度因子探索对应于每次训练尝试中第一层权重的不同初始化，从而实现了一种多起点全局搜索算法（尽管仅局限于少量权重），这有助于达到全局最小值。该方法已在对实际工业过程（无心磨削）结果进行建模的FFNN上进行了测试。