geneticProgramming: Standard typed and untyped genetic programming
In rgp: R genetic programming framework

Description Usage Arguments Value See Also

Perform a standard genetic programming (GP) run. Use geneticProgramming for untyped genetic programming or typedGeneticProgramming for typed genetic programming runs. The required argument fitnessFunction must be supplied with an objective function that assigns a numerical fitness value to an R function. Fitness values are minimized, i.e. smaller values denote higher/better fitness. If a multi-objective selectionFunction is used, fitnessFunction return a numerical vector of fitness values. The result of the GP run is a GP result object containing a GP population of R functions. summary.geneticProgrammingResult can be used to create summary views of a GP result object. During the run, restarts are triggered by the restartCondition. When a restart is triggered, the restartStrategy is executed, which returns a new population to replace the current one as well as a list of elite individuals. These are added to the runs elite list, where fitter individuals replace individuals with lesser fittness. The runs elite list is always sorted by fitness in ascending order. Only the first component of a multi-criterial fitness counts in this sorting. After a GP run, the population is inserted into the elite list. The elite list is returned as part of the GP result object.

geneticProgramming(fitnessFunction, stopCondition = makeTimeStopCondition(5),
  population = NULL, populationSize = 100, eliteSize = ceiling(0.1 *
  populationSize), elite = list(), functionSet = mathFunctionSet,
  inputVariables = inputVariableSet("x"), constantSet = numericConstantSet,
  crossoverFunction = crossover, mutationFunction = NULL,
  restartCondition = makeEmptyRestartCondition(),
  restartStrategy = makeLocalRestartStrategy(),
  searchHeuristic = makeAgeFitnessComplexityParetoGpSearchHeuristic(lambda =
  ceiling(0.5 * populationSize)), breedingFitness = function(individual) TRUE,
  breedingTries = 50, extinctionPrevention = FALSE, archive = FALSE,
  progressMonitor = NULL, verbose = TRUE)

typedGeneticProgramming(fitnessFunction, type,
  stopCondition = makeTimeStopCondition(5), population = NULL,
  populationSize = 100, eliteSize = ceiling(0.1 * populationSize),
  elite = list(), functionSet, inputVariables, constantSet,
  crossoverFunction = crossoverTyped, mutationFunction = NULL,
  restartCondition = makeEmptyRestartCondition(),
  restartStrategy = makeLocalRestartStrategy(populationType = type),
  searchHeuristic = makeAgeFitnessComplexityParetoGpSearchHeuristic(),
  breedingFitness = function(individual) TRUE, breedingTries = 50,
  extinctionPrevention = FALSE, archive = FALSE, progressMonitor = NULL,
  verbose = TRUE)

`fitnessFunction`	In case of a single-objective selection function, `fitnessFunction` must be a single function that assigns a numerical fitness value to a GP individual represented as a R function. Smaller fitness values mean higher/better fitness. If a multi-objective selection function is used, `fitnessFunction` must return a numerical vector of fitness values.
`type`	The range type of the individual functions. This parameter only applies to `typedGeneticProgramming`.
`stopCondition`	The stop condition for the evolution main loop. See code `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.
`functionSet`	The function set.
`inputVariables`	The input variable 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`.
`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.
`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.