mcga
Performs machine coded genetic algorithms on a function subject to be minimized.
Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems.
Usage
mcga(popsize, chsize, crossprob = 1.0, mutateprob = 0.01,
elitism = 1, minval, maxval, maxiter = 10, evalFunc)Arguments
- popsize
Number of chromosomes.
- chsize
Number of parameters.
- crossprob
Crossover probability. By default it is 1.0
- mutateprob
Mutation probability. By default it is 0.01
- elitism
Number of best chromosomes to be copied directly into next generation. By default it is 1
- minval
The lower bound of the randomized initial population. This is not a constraint for parameters.
- maxval
The upper bound of the randomized initial population. This is not a constraint for parameters.
- maxiter
The maximum number of generations. By default it is 10
- evalFunc
An R function. By default, each problem is a minimization.
Value
Sorted population resulted after generations
Cost values for each chromosomes in the resulted population
References
M.H.Satman (2013), Machine Coded Genetic Algorithms for Real Parameter Optimization Problems, Gazi University Journal of Science, Vol 26, No 1, pp. 85-95
Examples
# NOT RUN {
# A sample optimization problem
# Min f(xi) = (x1-7)^2 + (x2-77)^2 + (x3-777)^2 + (x4-7777)^2 + (x5-77777)^2
# The range of xi is unknown. The solution is
# x1 = 7
# x2 = 77
# x3 = 777
# x4 = 7777
# x5 = 77777
# Min f(xi) = 0
require("mcga")
f<-function(x){
return ((x[1]-7)^2 + (x[2]-77)^2 +(x[3]-777)^2 +(x[4]-7777)^2 +(x[5]-77777)^2)
}
m <- mcga( popsize=200,
chsize=5,
minval=0.0,
maxval=999999999.9,
maxiter=2500,
crossprob=1.0,
mutateprob=0.01,
evalFunc=f)
cat("Best chromosome:\n")
print(m$population[1,])
cat("Cost: ",m$costs[1],"\n")
# }