通过遗传自组装进化归纳概括
Evolving inductive generalization via genetic self-assembly
摘要：
我们建议，遗传编码的自组装组件大大提高了复杂系统的进化，并提供了一个有效的平台，归纳推广，即归纳推导的解决方案的问题，潜在的无限数量的实例从一组有限的测试示例。我们在模拟中不断发展的可扩展电路的几个问题。其中之一，数字乘法，近年来得到了深入研究，迄今为止，只有特定的小乘数的进化设计实现。事实上，这个问题和其他问题可以解决在充分的一般性，采用自组装揭示了自组装在生物学中的进化作用，是相关的纳米和生物纳米技术的复杂系统的设计。
英文摘要：
We propose that genetic encoding of self-assembling components greatly enhances the evolution of complex systems and provides an efficient platform for inductive generalization, i.e. the inductive derivation of a solution to a problem with a potentially infinite number of instances from a limited set of test examples. We exemplify this in simulations by evolving scalable circuitry for several problems. One of them, digital multiplication, has been intensively studied in recent years, where hitherto the evolutionary design of only specific small multipliers was achieved. The fact that this and other problems can be solved in full generality employing self-assembly sheds light on the evolutionary role of self-assembly in biology and is of relevance for the design of complex systems in nano- and bionanotechnology.
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