mcga2
is the improvement version of the standard mcga function as it is based on the GA::ga
function. The
byte_crossover
and the byte_mutation
operators are the main reproduction operators and these operators uses the byte
representations of parents in the computer memory.
1 2 3 4 5 6 7  mcga2(fitness, ..., min, max,
population = gaControl("realvalued")$population,
selection = gaControl("realvalued")$selection,
crossover = byte_crossover, mutation = byte_mutation, popSize = 50,
pcrossover = 0.8, pmutation = 0.1, elitism = base::max(1, round(popSize
* 0.05)), maxiter = 100, run = maxiter, maxFitness = Inf,
names = NULL, parallel = FALSE, monitor = gaMonitor, seed = NULL)

fitness 
The goal function to be maximized 
... 
Additional arguments to be passed to the fitness function 
min 
Vector of lower bounds of variables 
max 
Vector of upper bounds of variables 
population 
Initial population. It is 
selection 
Selection operator. It is 
crossover 
Crossover operator. It is 
mutation 
Mutation operator. It is 
popSize 
Population size. It is 50 by default 
pcrossover 
Probability of crossover. It is 0.8 by default 
pmutation 
Probability of mutation. It is 0.1 by default 
elitism 
Number of elitist solutions. It is 
maxiter 
Maximum number of generations. It is 100 by default 
run 
The genetic search is stopped if the best solution has not any improvements in last 
maxFitness 
Upper bound of the fitness function. By default it is Inf 
names 
Vector of names of the variables. By default it is 
parallel 
If TRUE, fitness calculations are performed parallel. It is FALSE by default 
monitor 
The monitoring function for printing some information about the current state of the genetic search. It is 
seed 
The seed for random number generating. It is 
Returns an object of class gaclass
Mehmet Hakan Satman  mhsatman@istanbul.edu.tr
M.H.Satman (2013), Machine Coded Genetic Algorithms for Real Parameter Optimization Problems, Gazi University Journal of Science, Vol 26, No 1, pp. 8595
Luca Scrucca (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 137. URL http://www.jstatsoft.org/v53/i04/
GA::ga
1 2 3 4 5 6 
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