论文标题
简单的遗传运算符是概率分布的通用近似值（以及表达编码的其他优势）
Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
论文作者
论文摘要
本文表征了进化算法的固有力量。该功率取决于遗传编码的计算特性。有了一些编码，两个父母与简单的跨界操作员重新组合可以从儿童表型的任意分布中进行样本。在本文中，此类编码称为\ emph {表达式编码}。通用函数近似值，包括遗传编程和神经网络的流行进化基板，可用于构建表达性编码。值得注意的是，这种方法不必仅应用于表型是一个函数的域：即使优化静态结构（例如二进制向量），也可以达到表达性。这样简单的设置使理论上表征表达性编码成为可能：在各种测试问题上，表达性编码显示可实现超过标准直接编码的超级指数收敛的加速。结论是，在像遗传编程，神经进化，遗传算法和理论的进化计算领域中，表达式编码可以成为理解和实现全部进化力量的关键。
This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents recombined with a simple crossover operator can sample from an arbitrary distribution of child phenotypes. Such encodings are termed \emph{expressive encodings} in this paper. Universal function approximators, including popular evolutionary substrates of genetic programming and neural networks, can be used to construct expressive encodings. Remarkably, this approach need not be applied only to domains where the phenotype is a function: Expressivity can be achieved even when optimizing static structures, such as binary vectors. Such simpler settings make it possible to characterize expressive encodings theoretically: Across a variety of test problems, expressive encodings are shown to achieve up to super-exponential convergence speed-ups over the standard direct encoding. The conclusion is that, across evolutionary computation areas as diverse as genetic programming, neuroevolution, genetic algorithms, and theory, expressive encodings can be a key to understanding and realizing the full power of evolution.
