multiNicheSymbolicRegression: Symbolic regression via multi-niche standard genetic...
In rgp: R genetic programming framework

Description Usage Arguments Value See Also

Perform symbolic regression via untyped multi-niche 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.

multiNicheSymbolicRegression(formula, data,
  stopCondition = makeTimeStopCondition(25),
  passStopCondition = makeTimeStopCondition(5), numberOfNiches = 2,
  clusterFunction = groupListConsecutive, joinFunction = function(niches)
  Reduce(c, niches), population = NULL, populationSize = 100,
  eliteSize = ceiling(0.1 * populationSize), elite = list(),
  individualSizeLimit = 64, penalizeGenotypeConstantIndividuals = FALSE,
  functionSet = mathFunctionSet, constantSet = numericConstantSet,
  selectionFunction = makeTournamentSelection(),
  crossoverFunction = crossover, mutationFunction = NULL,
  restartCondition = makeEmptyRestartCondition(),
  restartStrategy = makeLocalRestartStrategy(), progressMonitor = NULL,
  verbose = TRUE, clusterApply = sfClusterApplyLB,
  clusterExport = sfExport)

`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.
`passStopCondition`	The stop condition for each parallel pass. See `makeStepsStopCondition` for details.
`numberOfNiches`	The number of niches to cluster the population into.
`clusterFunction`	The function used to cluster the population into niches. The first parameter of this function is a GP population, the second paramater an integer representing the number of niches. Defaults to `groupListConsecutive`.
`joinFunction`	The function used to join all niches into a population again after a round of parallel passes. Defaults to a function that simply concatenates all niches.
`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.
`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`.
`functionSet`	The function set.
`constantSet`	The set of constant factory functions.
`selectionFunction`	The selection function to use. Defaults to tournament selection. See makeTournamentSelection for details.
`crossoverFunction`	The crossover function.
`mutationFunction`	The mutation function.
`restartCondition`	The restart condition for the evolution main loop. See makeFitnessStagnationRestartCondition for details.
`restartStrategy`	The strategy for doing restarts. See makeLocalRestartStrategy for details.
`progressMonitor`	A function of signature `function(population, objectiveVectors, fitnessFunction, stepNumber, evaluationNumber, bestFitness, timeElapsed, ...)` to be called with each evolution step. Seach heuristics may pass additional information via the `...` parameter.
`verbose`	Whether to print progress messages.
`clusterApply`	The cluster apply function that is used to distribute the parallel passes to CPUs in a compute cluster.
`clusterExport`	A function that is used to export R variables to the nodes of a CPU cluster, defaults to snowfall's `sfExport`.