Open Access
 Issue JNWPU Volume 38, Number 2, April 2020 434 - 441 https://doi.org/10.1051/jnwpu/20203820434 17 July 2020

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## 2 FORDA算法设计

### 2.3 森林播种过程

#### 2.3.1 本地播种

 图1本地播种过程

#### 2.3.3 远地播种

 图2远地播种过程

### 2.4 FORDA算法描述

1) 提取Data的条件属性及决策属性等关键因子, 构建决策信息系统S, 计算候选断点集Eh;

2) 初始化森林参数, 预设定森林适宜值参数m与阈值η、树数目Tnum、树的最大年龄Life-time、森林适宜值Forestopt、候选断点集个数n、本地播种参数LSC和Δx、远地播种参数GSC、迁移概率rate以及多数包含关系β等参数值; 则树的编码维度为n+1;

3) 随机产生Tnum个维度为n+1的实数编码树;

4) for i=1, 2…Tnum

5) 将森林中树Ti映射成断点集;

6) end for

7) While森林适宜值Forestopt变化值>阈值η:

8) 执行本地播种, 更新森林参数, 去除老化的树, 更新候选森林;

9) 执行远地播种, 更新森林参数, 计算各个树Ti的适宜值;

10) 根据树的适宜值, 计算并更新森林适宜值;

11) end While

12) 得到最优森林, for i=1, 2…Tnum

13) 计算Optimum(Ei)并排序;

14) end for

15) 适宜值为max(Optimum(Ei))的树, 即为最优树, 其对应多维最优断点集Eopt;

 图3FORDA流程图

## 3 实验

### 3.2 实验1

 图4各算法SVM分类精度
 图5不同LSC值下的分类精度

### 3.3 实验2

 图6FORDA时长与平均时长对比

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## All Figures

 图1本地播种过程 In the text
 图2远地播种过程 In the text
 图3FORDA流程图 In the text
 图4各算法SVM分类精度 In the text
 图5不同LSC值下的分类精度 In the text
 图6FORDA时长与平均时长对比 In the text

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