Description Usage Arguments Value See Also
View source: R/symbolic_regression.r
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.
1 2 3 4 5 6 7 8 9 10 11 12 13  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 
data 
A 
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 
The number of individuals if a population is to be created. 
eliteSize 
The number of elite individuals to keep. Defaults to

elite 
The elite list, must be alist of individuals sorted in ascending order by their first fitness component. 
extinctionPrevention 
When set to 
archive 
If set to 
individualSizeLimit 
Individuals with a number of tree nodes that
exceeds this size limit will get a fitness of 
penalizeGenotypeConstantIndividuals 
Individuals that do not contain
any input variables will get a fitness of 
subSamplingShare 
The share of fitness cases s sampled for evaluation with each function evaluation. 0 < s ≤q 1 must
hold, defaults to 
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 searchheuristic (i.e. optimization algorithm) to use
in the search of solutions. See the documentation for 
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 
breedingTries 
In case of a boolean 
errorMeasure 
A function to use as an error measure, defaults to RMSE. 
progressMonitor 
A function of signature

envir 
The R environment to evaluate individuals in, defaults to

verbose 
Whether to print progress messages. 
An symbolic regression model that contains an untyped GP population.
predict.symbolicRegressionModel
, geneticProgramming
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