# mcga-package: Machine Coded Genetic Algorithms for Real-valued Optimization... In mcga: Machine Coded Genetic Algorithms for Real-Valued Optimization Problems

## Description

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.

## Details

 Package: mcga Type: Package Version: 2.0.3 Date: 2012-01-06 License: GPL LazyLoad: yes

## Author(s)

Mehmet Hakan Satman

Maintainer: Mehmet Hakan Satman <[email protected]>

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```## 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") ## End(Not run) ```

mcga documentation built on May 29, 2017, 9:01 p.m.