论文标题
不断发展机器的行为：从微型到宏观进化
Evolving the Behavior of Machines: From Micro to Macroevolution
论文作者
论文摘要
进化产生了比人类设计最大的系统更复杂的生物。自计算出现以来，这项壮举激发了计算机科学家，并导致了可以发展机器的复杂神经网络的优化工具，即一种称为“神经进化”的方法。在为高维伪像设计可发展的表示形式方面取得了成功之后，最近通过超越优化而振兴了该领域：对于许多人来说，进化的奇观在每个物种的完美优化中都比在这种简单的迭代过程的创造力中的创造力中所获得的完美优化，即在物种的多样性中。这种人工进化的现代观点正在将田地从微观进化中移动，因为在利基市场中的健身梯度升至宏观进化，从而填补了许多高度不同物种的利基市场。它已经打开了有希望的应用程序，例如不断发展的步态曲目，不同口味的视频游戏水平以及用于空气动力自行车的各种设计。
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of computing and led to optimization tools that can evolve complex neural networks for machines -- an approach known as "neuroevolution". After a few successes in designing evolvable representations for high-dimensional artifacts, the field has been recently revitalized by going beyond optimization: to many, the wonder of evolution is less in the perfect optimization of each species than in the creativity of such a simple iterative process, that is, in the diversity of species. This modern view of artificial evolution is moving the field away from microevolution, following a fitness gradient in a niche, to macroevolution, filling many niches with highly different species. It already opened promising applications, like evolving gait repertoires, video game levels for different tastes, and diverse designs for aerodynamic bikes.
