mcga2: Performs a machine-coded genetic algorithm search for a given optimization problem

Description

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.

Usage

mcga2(fitness, ..., min, max,
  population = gaControl("real-valued")$population,
  selection = gaControl("real-valued")$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)

Value

Returns an object of class ga-class

Arguments

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 gaControl("real-valued")$population by default.
selection: Selection operator. It is gaControl("real-valued")$selection by default.
crossover: Crossover operator. It is byte_crossover by default.
mutation: Mutation operator. It is byte_mutation by default. Other values can be given including byte_mutation_random, byte_mutation_dynamic and byte_mutation_random_dynamic
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 base::max(1, round(popSize*0.05)) by default
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 run generations. By default it is maxiter
maxFitness: Upper bound of the fitness function. By default it is Inf
names: Vector of names of the variables. By default it is NULL
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 gaMonitor by default
seed: The seed for random number generating. It is NULL by default

Author

Mehmet Hakan Satman - mhsatman@istanbul.edu.tr

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

Luca Scrucca (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37.

Examples

Run this code

f <- function(x){ 
  return(-sum( (x-5)^2 ) )
}
myga <- mcga2(fitness = f, popSize = 100, maxiter = 300, 
              min = rep(-50,5), max = rep(50,5))
print(myga@solution)

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