plot.ga-method function

Plot of Genetic Algorithm search path

The plot method for ga-class objects gives a plot of best and average fitness values found during the iterations of the GA search. methods

## S4 method for signature 'ga'
plot(x, y, ylim, cex.points = 0.7, 
     col = c("green3", "dodgerblue3",  adjustcolor("green3", alpha.f = 0.1)),
     pch = c(16, 1), lty = c(1,2), legend = TRUE, grid = graphics::grid, ...)

Arguments

• x: An object of class "ga".
• y: Not used.
• ylim: A vector of two values specifying the limits on the y-axis.
• cex.points: The magnification to be used for points.
• col: The colours to be used for best and average fitness values.
• pch: The type of points to be used for best and average fitness values.
• lty: The type of lines to be used for best and average fitness values.
• legend: A logical specifying if a legend should be included.
• grid: A function for grid drawing of NULL to avoid drawing one.
• ...: Further arguments, currently not used.

Details

Plot best and average fitness values at each iteration of GA search.

Returns

The method invisibly return a data.frame with the iterations and summary statistics for the fitness function evaluated at each iteration.

See Also

ga, ga-class.

Author(s)

Luca Scrucca

Examples

# See examples in help(ga)

# The following code shows how to obtain graphs using the 
# ggplot2 plotting system 
## Not run:

GA <- ga(type = "real-valued", 
         fitness = function(x) -(abs(x)+cos(x)), 
         lower = -20, upper = 20, 
         popSize = 20, pmutation = 0.2, maxiter = 50)
out <- plot(GA)
library(reshape2)
df <- melt(out[,c(1:3,5)], id.var = "iter")
library(ggplot2)
ggplot(out) +
  geom_ribbon(aes(x = iter, ymin = median, ymax = max, 
                  colour = "median", fill = "median")) +
  geom_line(aes(x = iter, y = max, colour = "max")) +
  geom_point(aes(x = iter, y = max, colour = "max")) +
  geom_line(aes(x = iter, y = mean, colour = "mean"), lty = 2) +
  geom_point(aes(x = iter, y = mean, colour = "mean"), pch = 1) +
  xlab("Generation") +  ylab("Fitness values") +
  scale_colour_manual(breaks = c("max", "mean", "median"),
                      values = c("green3", "dodgerblue3", adjustcolor("green3", alpha.f = 0.1))) +
  scale_fill_manual(breaks = "median",
                    values = adjustcolor("green3", alpha.f = 0.1)) +
  guides(fill = "none", 
         colour = guide_legend(override.aes = 
                                 list(fill = c(NA, NA, adjustcolor("green3", alpha.f = 0.1)),
                                       pch = c(19,1,NA)))) +
  theme_bw() +
  theme(legend.title = element_blank(), 
        legend.pos = "top",
        legend.background = element_blank())
## End(Not run)