Description Usage Arguments Value Author(s) References See Also Examples

`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("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)
``` |

`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 `ga-class`

Mehmet Hakan Satman - [email protected]

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. URL http://www.jstatsoft.org/v53/i04/

GA::ga

1 2 3 4 5 6 |

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