进化机器的行为：从微观进化到宏观进化
Evolving the Behavior of Machines: From Micro to Macroevolution
摘要：
进化产生了比人类设计的最伟大的系统更复杂的生物。自从计算机出现以来，这一壮举一直激励着计算机科学家，并导致了可以为机器进化复杂神经网络的优化工具--一种被称为“神经进化”的方法。在为高维人工制品设计出可进化的表示方法方面取得了一些成功之后，这一领域最近通过超越优化而重新焕发了活力：对许多人来说，进化的奇迹与其说是每个物种的完美优化，不如说是这种简单迭代过程的创造力，即物种的多样性。这种人工进化的现代观点正在将领域从微观进化转移到宏观进化，沿着生态位中的适应度梯度，用高度不同的物种填充许多生态位。它已经开启了有前景的应用，比如不断发展的步态曲目，适合不同口味的视频游戏关卡，以及空气动力学自行车的多样化设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
