Genetic algorithm 用于分类和适应度评估的遗传算法

我一直在读汤姆·米切尔（Tom Mitchell）关于机器学习的书，这是用于分类的遗传算法的一部分。他们举的例子相当简单，他们说如果我有以下几点：

age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
fnlwgt: continuous.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education-num: continuous.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
capital-gain: continuous.
capital-loss: continuous.
hours-per-week: continuous.
native-country

那么适应度函数可以定义为：

我想将此方法应用于人口普查收入数据的分类，其形式如下：

39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, <=50K
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K

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最后，我想要的是有一个分类器，给定一些属性，它可以预测一个人的收入是小于还是大于50000。如何为这种情况建立适应度函数的模型？

通常，出于这种目的，使用遗传编程。下面是一篇描述这种情况的文章：

如果您正在寻找源代码，可以使用ricardo poli的Tiny GP：但是，首先必须将所有属性转换为数值

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您也可以使用其他GP变体。我在这里做了一个多表达式编程的实现：

通常，出于这种目的，使用遗传编程。下面是一篇描述这种情况的文章：

如果您正在寻找源代码，可以使用ricardo poli的Tiny GP：但是，首先必须将所有属性转换为数值

您也可以使用其他GP变体。我实现了多表达式编程，如下所示：