dataDrivenGeneticProgramming: Data-driven untyped standard genetic programming
In rgp: R genetic programming framework

Description Usage Arguments Value See Also

Perform an untyped genetic programming using a fitness function that depends on a R data frame. Typical applications are data mining tasks such as symbolic regression or classification. The task is specified as a formula and a fitness function factory. Only simple formulas without interactions are supported. The result of the data-driven GP run is a model structure containing the formulas and an untyped GP population. This function is primarily an intermediate for extensions. End-users will probably use more specialized GP tools such as symbolicRegression.

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)

`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.
`restartCondition`	The restart condition for the evolution main loop. See makeEmptyRestartCondition for details.
`restartStrategy`	The strategy for doing restarts. See makeLocalRestartStrategy for details.
`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.