利用R语言写一个基于模拟退火算法的特征选择算法，以支持向量机模型计算每次迭代的适应度函数值

以下是一个基于模拟退火算法的特征选择算法，使用支持向量机模型计算每次迭代的适应度函数值。代码中使用了iris数据集作为示例数据集。 ```R library(e1071) # 定义适应度函数 fitness <- function(features, data) { svm_model <- svm(Species ~ ., data[, features], kernel = "linear") accuracy <- mean(predict(svm_model, data[, features]) == data$Species) return(accuracy) } # 定义模拟退火算法 sa_feature_selection <- function(data, initial_features, t_max = 1000, t_min = 1e-8, alpha = 0.95, max_iter = 100) { # 初始化当前特征集合和适应度函数值 current_features <- initial_features current_fitness <- fitness(current_features, data) # 初始化最优特征集合和适应度函数值 best_features <- current_features best_fitness <- current_fitness # 初始化温度和迭代次数 t <- t_max iter <- 0 while (t > t_min && iter < max_iter) { # 随机选择一个特征进行翻转 new_features <- current_features flip_index <- sample(length(new_features), 1) new_features[flip_index] <- !new_features[flip_index] # 计算新特征集合的适应度函数值 new_fitness <- fitness(new_features, data) # 判断是否接受新解 delta_fitness <- new_fitness - current_fitness if (delta_fitness > 0 || exp(delta_fitness / t) > runif(1)) { current_features <- new_features current_fitness <- new_fitness } # 更新最优解 if (current_fitness > best_fitness) { best_features <- current_features best_fitness <- current_fitness } # 更新温度和迭代次数 t <- alpha * t iter <- iter + 1 } # 返回最优特征集