fitness <- function(curr, y, X, m)
{
# fitness function for SGA and PGA
# this one uses GCV
# by Mu Zhu
# Feb 2005
  n <- nrow(X); p <- ncol(X)
  gcvrss <- numeric(m)		
  for (i in 1:m)
  {
    q <- sum(curr[i,])
    if (q > 0) {
      ls.out <- lsfit(X[,curr[i,]==1], y, intercept=T)
      gcvrss[i]<- sum((ls.out$resid/(1-(q+1)/n))^2)
    }
    else {
      ybar <- mean(y)
      gcvrss[i]<- sum(((y-ybar)/(1-(q+1)/n))^2)
    }
  }
  return(gcvrss)
}