symbolicRegression: Symbolic regression via untyped standard genetic programming
In rgp: R genetic programming framework

Description Usage Arguments Value See Also

Perform symbolic regression via untyped genetic programming. The regression task is specified as a formula. Only simple formulas without interactions are supported. The result of the symbolic regression run is a symbolic regression model containing an untyped GP population of model functions.

symbolicRegression(formula, data, stopCondition = makeTimeStopCondition(5),
  population = NULL, populationSize = 100, eliteSize = ceiling(0.1 *
  populationSize), elite = list(), extinctionPrevention = FALSE,
  archive = FALSE, individualSizeLimit = 64,
  penalizeGenotypeConstantIndividuals = FALSE, subSamplingShare = 1,
  functionSet = mathFunctionSet, constantSet = numericConstantSet,
  crossoverFunction = NULL, mutationFunction = NULL,
  restartCondition = makeEmptyRestartCondition(),
  restartStrategy = makeLocalRestartStrategy(),
  searchHeuristic = makeAgeFitnessComplexityParetoGpSearchHeuristic(),
  breedingFitness = function(individual) TRUE, breedingTries = 50,
  errorMeasure = rmse, progressMonitor = NULL, envir = parent.frame(),
  verbose = TRUE)

`formula`	A `formula` describing the regression 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 symbolic regression run. The variables in `formula` must match column names in this data frame.
`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.
`individualSizeLimit`	Individuals with a number of tree nodes that exceeds this size limit will get a fitness of `Inf`.
`penalizeGenotypeConstantIndividuals`	Individuals that do not contain any input variables will get a fitness of `Inf`.
`subSamplingShare`	The share of fitness cases s sampled for evaluation with each function evaluation. 0 < s ≤q 1 must hold, defaults to `1.0`.
`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`.
`errorMeasure`	A function to use as an error measure, defaults to RMSE.
`progressMonitor`	A function of signature `function(population, fitnessValues, fitnessFunction, stepNumber, evaluationNumber, bestFitness, timeElapsed)` to be called with each evolution step.
`envir`	The R environment to evaluate individuals in, defaults to `parent.frame()`.
`verbose`	Whether to print progress messages.