# 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")
# }
```

*Documentation reproduced from package mcga, version 3.0.3, License: GPL (>= 2)*