multiNicheGeneticProgramming: Cluster-based multi-niche genetic programming
In rgp: R genetic programming framework

Description Usage Arguments Value See Also

Perform a multi-niche genetic programming run. 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 mean higher/better fitness. If a multi-objective selectionFunction is used, fitnessFunction return a numerical vector of fitness values. In a multi-niche genetic programming run, the initial population is clustered via a clusterFunction into numberOfNiches niches. In each niche, a genetic programming run is executed with passStopCondition as stop condition. These runs are referred to as a parallel pass. After each parallel pass, the niches are joined again using a joinFunction into a population. From here, the process starts again with a clustering step, until the global stopCondition is met. The result of the multi-niche genetic programming run is a genetic programming result object containing a GP population of R functions. summary.geneticProgrammingResult can be used to create summary views of a GP result object.

multiNicheGeneticProgramming(fitnessFunction,
  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(),
  functionSet = mathFunctionSet, inputVariables = inputVariableSet("x"),
  constantSet = numericConstantSet, crossoverFunction = crossover,
  mutationFunction = NULL, restartCondition = makeEmptyRestartCondition(),
  restartStrategy = makeLocalRestartStrategy(),
  searchHeuristic = makeAgeFitnessComplexityParetoGpSearchHeuristic(),
  progressMonitor = NULL, verbose = TRUE, clusterApply = sfClusterApplyLB,
  clusterExport = sfExport)

`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.
`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.
`functionSet`	The function set.
`inputVariables`	The input variable set.
`constantSet`	The set of constant factory functions.
`searchHeuristic`	The search-heuristic (i.e. optimization algorithm) to use in the search of solutions. See the documentation for `searchHeuristics` for available algorithms.
`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 `sfExport`.