Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic architecture optimization methods help to find the most suitable design, but they are not extensively adopted because of their high computational cost. In this work, we introduce the Random Error Sampling-based Neuroevolution (RESN), an evolutionary algorithm that uses the mean absolute error random sampling, a training-free approach to predict the expected performance of an artificial neural network, to optimize the architecture of a network. We empirically validate our proposal on four prediction problems, and compare our technique to training-based architecture optimization techniques, neuroevolutionary approaches, and expert designed solutions. Our findings show that we can achieve state-of-the-art error performance and that we reduce by half the time needed to perform the optimization.

递归神经网络擅长解决预测问题。但是，找到适合问题的网络非常困难，因为其性能受体系结构配置的强烈影响。自动体系结构优化方法有助于找到最合适的设计，但由于计算量大，因此并未得到广泛采用。在这项工作中，我们介绍了基于随机误差采样的神经进化（RESN），这是一种使用平均绝对误差随机采样的进化算法，该算法是一种无需训练的方法来预测人工神经网络的预期性能，以优化神经网络的架构。一个网络。我们根据经验对四个预测问题验证了我们的建议，并将我们的技术与基于训练的架构优化技术，神经进化方法，和专家设计的解决方案。我们的发现表明，我们可以实现最新的错误性能，并且将执行优化所需的时间减少了一半。