Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming
Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming
复制标题
使用遗传编程将概率分布自动设计为进化规划的变异算子
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
E. Özcan
中科院分区：
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作者：
Libin Hong;J. Woodward;Jingpeng Li;E. Özcan
突变算子是进化编程变化的唯一来源。过去，这些被提名为人类，包括高斯，库奇和征税分布。我们使用遗传编程自动设计突变操作员（概率分布）。这是通过将标准高斯随机数生成器作为终端集和基本算术运算符作为函数集来完成的。换句话说，任意随机数发生器是通过遗传编程生成的任意函数传递的随机（高斯）生成的数字的函数。
我们考虑为特定功能类别定制突变操作员，而不是从事为任意基准函数开发突变操作员的徒劳尝试（这是由于无免费午餐定理的结果）。我们从函数类（一组函数上的概率分布）中汲取函数。突变概率分布是在从给定功能类中绘制的一组功能实例上训练的。然后，在单独的独立测试集实例上测试了它，以确认进化的概率分布确实已推广到功能类别。
初始结果令人鼓舞：在十个功能类别中的每个类别中，使用遗传编程生成的概率分布都优于高斯和库奇分布。
The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and included the Gaussian, Cauchy, and the Levy distributions. We automatically design mutation operators (probability distributions) using Genetic Programming. This is done by using a standard Gaussian random number generator as the terminal set and and basic arithmetic operators as the function set. In other words, an arbitrary random number generator is a function of a randomly (Gaussian) generated number passed through an arbitrary function generated by Genetic Programming.
Rather than engaging in the futile attempt to develop mutation operators for arbitrary benchmark functions (which is a consequence of the No Free Lunch theorems), we consider tailoring mutation operators for particular function classes. We draw functions from a function class (a probability distribution over a set of functions). The mutation probability distribution is trained on a set of function instances drawn from a given function class. It is then tested on a separate independent test set of function instances to confirm that the evolved probability distribution has indeed generalized to the function class.
Initial results are highly encouraging: on each of the ten function classes the probability distributions generated using Genetic Programming outperform both the Gaussian and Cauchy distributions.