function(population, fitnessFunction, stepNumber, evaluationNumber,
bestFitness, timeElapsed). They are used to decide when to restart a GP evolution run that
might be stuck in a local optimum. Evolution restart conditions are objects of the same
type and class as evolution stop conditions. They may be freely substituted for each other.
makeEmptyRestartCondition()
makeStepLimitRestartCondition(stepLimit = 10)
makeFitnessStagnationRestartCondition(fitnessHistorySize = 100, testFrequency = 10, fitnessStandardDeviationLimit = 1e-06)
makeFitnessDistributionRestartCondition(testFrequency = 100, fitnessStandardDeviationLimit = 1e-06)makeStepLimitRestartCondition.makeFitnessStagnationRestartCondition.makeFitnessStagnationRestartCondition.makeEmptyRestartCondition creates a restart condition that is never fulfilled, i.e.
restarts will never occur.
makeStepLimitRestartCondition creates a restart condition that holds if the
number if evolution steps is an integer multiple of a given step limit.
restarts will never occur.
makeFitnessStagnationRestartCondition creates a restart strategy that holds if the
standard deviation of a last fitnessHistorySize best fitness values falls below
a given fitnessStandardDeviationLimit.
makeFitnessDistributionRestartCondition creates a restart strategy that holds
if the standard deviation of the fitness values of the individuals in the current
population falls below a given fitnessStandardDeviationLimit.