Deep learning is a timely research direction in machine learning, where breakthrough progress has been made in both academe and industries, bringing promising results in speech recognition, computer vision, industrial control and automation, etc. The motivation of deep learning is primarily to establish a model to simulate the neural connection structure of the human brain. While dealing with complex tasks, deep learning adopts a number of transformation stages to deliver the in-depth description and interpretation of the data. Deep learning achieves exceptional power and flexibility by learning to represent the task through a nested hierarchy of layers, with more abstract representations formed successively in terms of less abstract ones. One of the key issues of existing deep learning approaches is that the meaningful representations can be learned only when their hyperparameter settings are properly specified beforehand, and general parameters are learned during the training process. Until now, not much research has been dedicated to automatically set the hyperparameters, and accurately find the globally optimal general parameters. However, this problem can be formulated as optimization problems, including discrete optimization, constrained optimization, large-scale global optimization, and multiobjective optimization, by engaging mechanisms of evolutionary computation.

中文翻译：

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客座编辑进化计算遇到深度学习

深度学习是机器学习的一个及时研究方向，无论是学术界还是工业界都取得了突破性进展，在语音识别、计算机视觉、工业控制和自动化等方面取得了可喜的成果。模型来模拟人脑的神经连接结构。在处理复杂任务时，深度学习采用多个转换阶段来提供对数据的深入描述和解释。深度学习通过学习通过嵌套的层级结构来表示任务，并根据不太抽象的表示依次形成更抽象的表示，从而实现了非凡的能力和灵活性。现有深度学习方法的关键问题之一是，只有事先正确指定超参数设置才能学习有意义的表示，并且在训练过程中学习通用参数。到目前为止，还没有多少研究致力于自动设置超参数，并准确地找到全局最优的通用参数。然而，通过引入进化计算机制，这个问题可以被表述为优化问题，包括离散优化、约束优化、大规模全局优化和多目标优化。没有太多的研究致力于自动设置超参数，并准确地找到全局最优的通用参数。然而，通过引入进化计算机制，这个问题可以被表述为优化问题，包括离散优化、约束优化、大规模全局优化和多目标优化。没有太多的研究致力于自动设置超参数，并准确地找到全局最优的通用参数。然而，通过引入进化计算机制，这个问题可以被表述为优化问题，包括离散优化、约束优化、大规模全局优化和多目标优化。