KSLCore/ksl.simopt.evaluator/SolutionsIfc/possiblyBest

possiblyBest

open fun possiblyBest(comparator: <Error class: unknown class><Solution>): Solutions(source)

Returns a list of solutions that are possibly the best by using the supplied comparator.

Parameters

comparator

the comparator to use to compare the solutions

open fun possiblyBest(level: Double = DEFAULT_CONFIDENCE_LEVEL, indifferenceZone: Double = 0.0): Solutions(source)

Returns a list of solutions that are possibly the best by using a PenalizedObjectiveFunctionConfidenceIntervalComparator. The basic procedure is to select the smallest or largest solution as the best dependent on the objective. Then, this procedure uses the best solution as the standard and compares all the solutions with it in a pair-wise manner. Any solutions that are considered not statistically different from the best solution are returned. The confidence interval is for each individual comparison with the best. Thus, to control the overall confidence, users will want to adjust the individual confidence interval level such that the overall confidence in the process is controlled. See the theory of related to multi-comparison discussed here The process used here is approximate.

Parameters

level

the level of confidence to use. By default, this is set to DEFAULT_CONFIDENCE_LEVEL.

indifferenceZone

the indifference zone to use. By default, this is set to 0.0.