Description Usage Arguments Details Value References See Also Examples

Approximately minimizes the value of a function using a simple heuristic optimizer that uses a combination of genetic and simulated annealing optimization.

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`fun` |
function that returns a numerical value. |

`population` |
a matrix or a list. If a matrix, then the rows correspond to the parameters and the columns are different parameter vectors. If a list, then it should have a component named "population" and
optionally a component named "objective" – in particular, it can
be the result of a call to |

`lower` |
vector giving the lower bound for parameter values. This is replicated to be as long as the number of parameters. |

`upper` |
vector giving the upper bound for parameter values. This is replicated to be as long as the number of parameters. |

`scale` |
vector of scales to use when doing local search with a solution. This is replicated to be as long as the number of parameters. |

`add.args` |
list of additional arguments to |

`control` |
an object like the output of |

`...` |
arguments for |

There is a `summary`

method for class `genopt`

which
shows the call, a summary of the set of objectives found, and the
best solution (set of parameters).

a list of class `genopt`

with the following components:

`population ` |
a matrix of the same size as the input population matrix, but generally with different values in it. |

`objective ` |
a vector with length equal to the number of columns of |

`funevals ` |
the number of function evaluations performed.
If |

`random.seed ` |
the random seed at the start of the call – given so that you can reproduce the computations. |

`call ` |
an image of the call that created this object. |

The original version of this function appeared in "S Poetry".

`genopt.control`

, `summary.genopt`

.

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