Entropy.R

# make 3D perspective plot
entropy <- function(x,y)
{
  -(0.5*x*log(y)+0.5*(1-x)*log(1-x)+(1-0.5)*y*log(y)+(1-0.5)*(1-y)*log(1-y))
}
#p <- x <- seq(0,.5, by=.1)
p <- 0.5;
x <- seq(0,.5, by=.1)
y <- seq(0,.5, by=.1)
f <- outer(x, y, entropy)
library(GA)

## Loading required package: foreach

## Loading required package: iterators

## Package 'GA' version 3.1.1
## Type 'citation("GA")' for citing this R package in publications.

persp3D(x, y, f, theta = 50, phi = 20, color.palette = rainbow);

# Optimise with GA
entropy2 <- function(p,x,y)
{
  -(p*x*log(x)+p*(1-x)*log(1-x)+(1-p)*y*log(y)+(1-p)*(1-y)*log(1-y))
}

GA <- ga(type = "real-valued", fitness = function(x) entropy2(x[1], x[2],x[3]), lower = c(0,0,0), upper = c(0.5, 0.5, 0.5), popSize = 50, maxiter = 100, optim = F)
summary(GA)

## ── Genetic Algorithm ─────────────────── 
## 
## GA settings: 
## Type                  =  real-valued 
## Population size       =  50 
## Number of generations =  100 
## Elitism               =  2 
## Crossover probability =  0.8 
## Mutation probability  =  0.1 
## Search domain = 
##        x1  x2  x3
## lower 0.0 0.0 0.0
## upper 0.5 0.5 0.5
## 
## GA results: 
## Iterations             = 100 
## Fitness function value = 0.6929294 
## Solution = 
##               x1        x2        x3
## [1,] 0.001671553 0.3443958 0.4917633