When I give a talk about evolutionary machine learning, one question I often expect is why I use an evolutionary algorithm to optimize the hyperparameters and structure of a neural network, rather than using a reinforcement learning algorithm. A quick answer might be, well, I am an evolutionary computation guy. I know this is a sloppy answer. Often, I attempt to explain the potential benefits of using an evolutionary algorithm in comparison with a reinforcement learning algorithm, e.g., in handling multiple objectives, in parallelizing the calculations, and also in dealing with sparse environmental feedback, among others. Clearly, it is always problem-dependent whether an evolutionary algorithm or a reinforcement learning algorithm should be chosen to solve a machine learning problem.

中文翻译：

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不要玩游戏，要优化【总裁寄语】

当我谈论进化机器学习时，我经常想到的一个问题是为什么我使用进化算法来优化神经网络的超参数和结构，而不是使用强化学习算法。一个快速的答案可能是，嗯，我是一个进化计算专家。我知道这是一个草率的答案。我经常尝试解释使用进化算法与强化学习算法相比的潜在好处，例如，在处理多个目标、并行计算以及处理稀疏环境反馈等方面。显然，选择进化算法还是强化学习算法来解决机器学习问题始终取决于问题。