View source: R/evaluate_fitness.R

This function uses default or user supplied ojective functions, rank functions, to evaluate fitness of a generation chromosomes. This process can be parallized if `nCores > 1`

. Parallel operations uses `mclapply`

to parallelize operations. Max number of parallel operations is determined by `detectCores`

. Prescheduling = TRUE is only option for compuations. It is advised to only use parallelization if inputed a large dataframe with great than 1000 oberations and/or predictors.

1 2 | ```
evaluate_fitness(generation_t0, Y, X, family, nCores, minimize,
objective_function, rank_objective_function)
``` |

`generation_t0` |
a matrix of parent chromosomes to be evaluated. Columns correspond to predictors/genes and rows correspond to parents/chromosomes. |

`Y` |
vector of response variable |

`X` |
a matrix or dataframe of predictor variables |

`family` |
a character string describing the error distribution and link function to be used in the model. Default is gaussian. |

`nCores` |
an integer indicating number of parallel processes to run when evaluating fitness. See |

`minimize` |
a logical value indicating whether optimize should be minimized (TRUE) or maximized (FALSE). |

`objective_function` |
function for computing objective. Default is |

`rank_objective_function` |
a function that ranks parents by their fitness as determined by optimize criteria. This function uses |

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