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方法，通过遗传算法对输入特征进行最优重缩放，以提高前馈神经网络的训练效率和泛化性能。该最优重缩放方法重塑输入空间，改善了用于训练的梯度下降算法的条件性。此外，遗传算法试验与选择所引入的缩放因子探索，相当于在每个训练尝试中对第一层权重进行不同初始化，从而实现了多起点全局搜索算法（尽管仅局限于少量权重），促进了全局最小值的达成。该方法已在一个模拟真实工业过程（无心磨削）结果的前馈神经网络上进行了测试。