Modern computers allow a methodical search of possibly billions of experiments and the exploitation of interactions that are not known in advance. This enables a bottom-up process of design by assembling or configuring systems and testing the degree to which they fulfill the desired goal. We give two detailed examples of this process. One is referred to as Cartesian genetic programming and the other evolution-in-materio. In the former, evolutionary algorithms are used to exploit the interactions of software components representing mathematical, logical, or computational elements. In the latter, evolutionary algorithms are used to manipulate physical systems particularly at the electrical or electronic level. We compare and contrast both approaches and discuss possible new research directions by borrowing ideas from one and using them in the other.

中文翻译：

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计算的炼金术：未知的设计

现代计算机可以对可能进行的数十亿次实验进行有条不紊的搜索，并且可以利用事先未知的交互作用。这使得自下而上通过组装或配置系统并测试其达到期望目标的程度进行设计的过程。我们给出此过程的两个详细示例。一个被称为笛卡尔遗传程序设计，另一个被称为同质进化。在前者中，进化算法用于开发代表数学，逻辑或计算元素的软件组件之间的相互作用。在后者中，进化算法用于操纵物理系统，尤其是在电气或电子层面。我们比较和对比这两种方法，并通过借鉴一种方法的思想并在另一种方法中使用它们来讨论可能的新研究方向。