如何从GA超参数调整中获得RMSE值？

我找到了使用GA进行超参数调整的R代码。下面是代码，但它没有显示预期的结果，这将是预测精度？我在问题的末尾提到了它产生的输出，但我希望输出的RMSE值为，0.44、0.23、0.1等

代码如下：

d=readARFF("soft.arff")
index <- createDataPartition(d$Effort, p = .70,list = FALSE)
tr <- d[index, ]
ts <- d[-index, ] 
svm_fit <- function(x) {
  mod <- train(Rank ~ ., data = tr,
               method = "svmRadial",
               preProc = c("center", "scale"),
               trControl = trainControl(method = "cv"),
               tuneGrid = data.frame(C = 2^x[1], sigma = exp(x[2])))
  -getTrainPerf(mod)[, "TrainRMSE"]
}

svm_ga_obj <- GA::ga(type = "real-valued", 

                            fitness = svm_fit,              

                                  min = c(-5, -5), 
                                  max = c(5, 0), 
                                  popSize = 50, 
                                  maxiter = 2,
                                  seed = 16478,
                                  keepBest = TRUE,
                                  monitor = NULL,
                                  elitism = 2)

summary(svm_ga_obj)

---

问题是什么？我如何获得RMSE的值？

请包括您用于此代码的程序包，以运行库（e1071）库（mlr）库（插入符号）、库（GA）、库（farff）

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  2 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
      x1 x2
lower -5 -5
upper  5  0

GA results: 
Iterations             = 2 
Fitness function value = -6309.072 
Solution = 
          x1        x2
[1,] 4.80478 -4.202595