Usage
dataDrivenGeneticProgramming(formula, data, fitnessFunctionFactory, fitnessFunctionFactoryParameters = list(), stopCondition = makeTimeStopCondition(5), population = NULL, populationSize = 100, eliteSize = ceiling(0.1 * populationSize), elite = list(), extinctionPrevention = FALSE, archive = FALSE, functionSet = mathFunctionSet, constantSet = numericConstantSet, crossoverFunction = NULL, mutationFunction = NULL, restartCondition = makeEmptyRestartCondition(), restartStrategy = makeLocalRestartStrategy(), searchHeuristic = makeAgeFitnessComplexityParetoGpSearchHeuristic(), breedingFitness = function(individual) TRUE, breedingTries = 50, progressMonitor = NULL, verbose = TRUE)
Arguments
formula
A formula describing the task. Only simple
formulas of the form response ~ variable1 + ... + variableN
are supported at this point in time. data
A data.frame containing training data for the
GP run. The variables in formula must match column names in this
data frame. fitnessFunctionFactory
A function that accepts two parameters, a
codeformula, data (given as a model frame) and the additional parameters
given in fitnessFunctionFactoryParameters and returns a fitness function. fitnessFunctionFactoryParameters
Additional parameters to pass to the
fitnessFunctionFactory.
stopCondition
The stop condition for the evolution main loop. See
makeStepsStopCondition for details.
population
The GP population to start the run with. If this parameter
is missing, a new GP population of size populationSize is created
through random growth.
populationSize
The number of individuals if a population is to be
created.
eliteSize
The number of elite individuals to keep. Defaults to
ceiling(0.1 * populationSize).
elite
The elite list, must be alist of individuals sorted in ascending
order by their first fitness component.
extinctionPrevention
When set to TRUE, the initialization and
selection steps will try to prevent duplicate individuals
from occurring in the population. Defaults to FALSE, as this
operation might be expensive with larger population sizes.
archive
If set to TRUE, all GP individuals evaluated are stored in an
archive list archiveList that is returned as part of the result of this function.
functionSet
The function set.
constantSet
The set of constant factory functions.
crossoverFunction
The crossover function.
mutationFunction
The mutation function.
searchHeuristic
The search-heuristic (i.e. optimization algorithm) to use
in the search of solutions. See the documentation for searchHeuristics for
available algorithms.
breedingFitness
A "breeding" function. This function is applied after
every stochastic operation Op that creates or modifies an individal
(typically, Op is a initialization, mutation, or crossover operation). If
the breeding function returns TRUE on the given individual, Op is
considered a success. If the breeding function returns FALSE, Op
is retried a maximum of breedingTries times. If this maximum number of
retries is exceeded, the result of the last try is considered as the result of
Op. In the case the breeding function returns a numeric value, the breeding
is repeated breedingTries times and the individual with the lowest breeding
fitness is considered the result of Op.
breedingTries
In case of a boolean breedingFitness function, the
maximum number of retries. In case of a numerical breedingFitness function,
the number of breeding steps. Also see the documentation for the breedingFitness
parameter. Defaults to 50.
progressMonitor
A function of signature
function(population, fitnessfunction, stepNumber, evaluationNumber,
bestFitness, timeElapsed) to be called with each evolution step.
verbose
Whether to print progress messages.