Description Usage Arguments Objects from the Class Slots Methods Author(s) References See Also Examples

Objects of the `GenAlg`

class represent one step (population) in the
evolution of a genetic algorithm. This algorithm has been customized to
perform feature selection for the class prediction problem.

1 2 3 4 5 6 |

`object` |
object of class |

`x` |
object of class |

`row.names` |
character vector giving the row names for the data frame,
or |

`optional` |
logical scalar. If |

`...` |
extra arguments for generic routines |

Objects should be created by calls to the `GenAlg`

generator;
they will also be created automatically as a result of applying the function
`newGeneration`

to an existing `GenAlg`

object.

`data`

:The initial population of potential solutions, in the form of a data matrix with one individual per row.

`fitfun`

:A function to compute the fitness of an individual solution. Must take two input arguments: a vector of indices into the rows of the population matrix, and a

`context`

list within which any other items required by the function can be resolved. Must return a real number; higher values indicate better fitness, with the maximum fitness occurring at the optimal solution to the underlying numerical problem.`mutfun`

:A function to mutate individual alleles in the population. Must take two arguments: the starting allele and a

`context`

list as in the fitness function.`p.mutation`

:numeric scalar between

`0`

and`1`

, representing the probability that an individual allele will be mutated.`p.crossover`

:numeric scalar between

`0`

and`1`

, representing the probability that crossover will occur during reproduction.`generation`

:integer scalar identifying the current generation.

`fitness`

:numeric vector containing the fitness of all individuals in the population.

`best.fit`

:A numeric value; the maximum fitness.

`best.individual`

:A matrix (often with one row) containing the individual(s) achieving the maximum fitness.

`context`

:A list of additional data required to perform mutation or to compute fitness. This list is passed along as the second argument when

`fitfun`

and`mutfun`

are called.

- as.data.frame
`signature(x = "GenAlg")`

: Converts the`GenAlg`

object into a data frame. The first column contains the fitness ; remaining columns contain three selected features, given as integer indices into the rows of the original data matrix.- as.matrix
`signature(x = "GenAlg")`

: Converts the GenAlg object into a matrix, following the conventions of`as.data.frame`

.- summary
`signature(object = "GenAlg")`

: Print a summary of the GenAlg object.

Kevin R. Coombes krc@silicovore.com, P. Roebuck proebuck@mdanderson.org

David Goldberg.

"Genetic Algorithms in Search, Optimization and Machine Learning."

Addison-Wesley, 1989.

1 | ```
showClass("GenAlg")
``` |

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