Last data update: 2014.03.03
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R: Plot of Genetic Algorithm search path
plot.ga-method | R Documentation |
Plot of Genetic Algorithm search path
Description
The plot method for ga-class objects gives a plot
of best and average fitness values found during the iterations of the
GA search.
Usage
## 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" .
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y |
Not used.
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ylim |
A vector of two values specifying the limits on the y-axis.
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cex.points |
The magnification to be used for points.
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col |
The colors to be used for best and average fitness values.
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pch |
The type of points to be used for best and average fitness values.
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lty |
The type of lines to be used for best and average fitness values.
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legend |
A logical specifying if a legend should be included.
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grid |
A function for grid drawing of NULL to avoid drawing one.
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... |
Further arguments, currently not used.
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Details
Plot best and average fitness values at each iteration of GA search.
Value
The method invisibly return a data.frame with the iterations and summary statistics for the fitness function evaluated at each iteration.
Author(s)
Luca Scrucca
See Also
ga , ga-class .
Examples
# See examples in help(ga)
# The following code shows how to obtain graphs using the
# ggplot2 plotting system
## Not run:
GA <- ga(...)
out <- plot(GA)
library(reshape2)
df <- melt(out[,c(1:3,5)], id.var = "iter")
library(ggplot2)
ggplot(df, aes(x = iter, y = value, group = variable, colour = variable)) +
xlab("Generation") + ylab("Fitness values") +
geom_point(aes(shape = variable)) +
geom_line(aes(lty = variable)) +
scale_colour_brewer(palette = "Set1") +
theme_bw() +
theme(legend.title = element_blank())
## End(Not run)
Results
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