MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
MCScheduling.GeneticAlgorithm.CAlternatingPositionCrossoverThe alternating position crossover operator implementation
MCScheduling.GeneticAlgorithm.CBaseBreederA breeder evolves a population by performing genetic operations defined in the specified configuration of the genetic algorithm
MCScheduling.GeneticAlgorithm.CBaseCrossoverOperatorA base class for implementation of a crossover genetic operator
MCScheduling.GeneticAlgorithm.CBaseGeneticAlgorithmA base class for implementation of a genetic algorithm
MCScheduling.MixedCriticality.CBaseMixedCriticalitySolverThe basic implementation of the IMixedCriticalitySolver
MCScheduling.GeneticAlgorithm.CBaseMutationOperatorA base class for implementation of a mutation genetic operator
MCScheduling.GeneticAlgorithm.CBaseSelectionOperatorA base class for implementation of a natural selector
MCScheduling.SimulatedAnnealing.CBaseSimulatedAnnealing< T >The base simulated annealing algorithm
MCScheduling.GeneticAlgorithm.CBoltzmannScalingIn case one wants the selection preasure to vary during the evolution he uses Bolzmann scaling
MCScheduling.GeneticAlgorithm.CChromosomePoolThe basic implementation of the IChromosomePool interface
MCScheduling.MixedCriticality.CEDF.CClairvoyantEDFSolverThe mixed-criticality scheduling problem solver based on the clairvoyant non-preemptive EDF scheduling algorithm
MCScheduling.GeneticAlgorithm.CConfigurationThe configuration represents a setting of a genetic algorithm, i.e
MCScheduling.MixedCriticality.CConsoleSolverThe command line interface for the MCScheduling application
MCScheduling.GeneticAlgorithm.CCrossoverOperatorSerializerA serializer for crossover operators
MCScheduling.GeneticAlgorithm.CCycleCrossoverThe cycle crossover operator implementation
MCScheduling.GeneticAlgorithm.CDefaultBreederA default implementation of IBreeder interface
MCScheduling.MixedCriticality.CDefaultMixedCriticalityInstanceGeneratorThe default implemenation of the IMixedCriticalityInstanceGenerator interface
MCScheduling.GeneticAlgorithm.CDeterministicSamplingThe deterministic sampling selection operator implementation
MCScheduling.GeneticAlgorithm.CDisplacedInversionMutationThe displaced inversion mutation operator
MCScheduling.GeneticAlgorithm.CDisplacementMutationThe displacement mutation operator
MCScheduling.MixedCriticality.DP.CDPSolverAn dynamic programming type algorithm for solving mixed-criticality scheduling problems of minimal size
MCScheduling.GeneticAlgorithm.CExchangeMutationThe exchange mutation operator
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CExponentialAcceptorClassic exponential distribution acceptor
MCScheduling.Utils.CExponentialRandomA exponential distribution random number generator
MCScheduling.GeneticAlgorithm.CFitnessEvaluatorSerializerThe serializer for the implemented finess evalutors
MCScheduling.GeneticAlgorithm.CFitnessScalerSerializerThe serializer for the implemented finess scalers
MCScheduling.MixedCriticality.GA.CGeneticAlgorithmSolverThe genetic algorihm based solver for the mixed-criticality scheduling
MCScheduling.GeneticAlgorithm.CGeneticOperatorThe base public class for implementation of a genetic operator
MCScheduling.GeneticAlgorithm.CConfiguration.CGreaterFitterComparerThe default fitness comparer
MCScheduling.GeneticAlgorithm.CGreedyCrossoverThe greedy crossover operator implementation
MCScheduling.GeneticAlgorithm.CHeuristicCrossoverThe heuristic crossover operator implementation
MCScheduling.GeneticAlgorithm.CInsertionMutationThe insertion mutation operator
MCScheduling.GeneticAlgorithm.CInversionMutationThe inversion mutation operator
MCScheduling.ClairvoyantEDF.ClairvoyantEDFThe mixed-criticality clairvoyant EDF algorithm implementation
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CLowerBetterComparerLower energy - lower makespan / lateness - better solution
MCScheduling.GeneticAlgorithm.CMersenneTwisterThe Mersenne Twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto and Takuji Nishimura
MCScheduling.MixedCriticality.GA.CMinimizeMakespanFitnessEvaluatorEvaluates the chromosome representing a schedule for a mixed-criticality instance according to its total lateness and maximum completion time, i.e
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CMinimumMakespanLatenessEvaluatorEvaluates the energy of the given state as a convex sum of the makespan and lateness of the corresponding solution schedule
MCScheduling.MixedCriticality.GA.CMixedCriticalityChromosomeThe CMixedCriticalityChromosome instance represents a schedule for a mixed-criticality instance that is associated with its genes which should be instances of CMixedCriticalityGene
MCScheduling.MixedCriticality.GA.CMixedCriticalityConfigurationThe CMixedCriticalityConfiguration represents a configuration of a genetic algorithm for solving Mixed-criticality Scheduling Problems
MCScheduling.MixedCriticality.GA.CMixedCriticalityGeneThe mixed-criticality gene represents a place in a schedule for a mixed-criticality job
MCScheduling.MixedCriticality.GA.CMixedCriticalityGeneticAlgorithm.CMixedCriticalityGeneDistanceMeasurerThe gene distance measurer for mixed-criticality genetic algorithm
MCScheduling.MixedCriticality.GA.CMixedCriticalityGeneticAlgorithmThe implementation of a genetic algorithm to solve mixed-criticality scheduling problems
MCScheduling.MixedCriticality.CMixedCriticalityInstanceRepresentation of a mixed-criticality instance
MCScheduling.MixedCriticality.CMixedCriticalityInstancesSerializerCMixedCriticalityInstancesSerializer provides a methods to serialize and deserialize mixed-criticality instances
MCScheduling.MixedCriticality.CMixedCriticalityJobA CJob instance represents a mixed-criticality job
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealingThe simulated annealing solver for the mixed-criticality scheduling
MCScheduling.MixedCriticality.MIP.CMixedIntegerProgrammingSolverThe mixed integer programming solver for the mixed-criticality scheduling
MCScheduling.GeneticAlgorithm.CMutationOperatorSerializerA serializer for mutation operators
MCScheduling.GeneticAlgorithm.CNullFitnessScalerA fitness scaler that leaves the fitness value as they are - does not scale
MCScheduling.GeneticAlgorithm.COrderBasedCrossoverThe order-based crossover operator implementation
MCScheduling.GeneticAlgorithm.COrderCrossoverThe order crossover operator implementation
MCScheduling.GeneticAlgorithm.CPartiallyMappedCrossoverThe partially mapped crossover operator implementation
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CPerturberThe perturber used to change one state into one of its "neighbouring" state
MCScheduling.GeneticAlgorithm.CPopulationA population consist of a individual chromosomes that forms potential solutions to a problem
MCScheduling.GeneticAlgorithm.CPositionBasedCrossoverThe postion-based crossover operator implementation
MCScheduling.GeneticAlgorithm.CPriorityQueue< T >A priority queue implementation based on a binary heap structure, which guarantees that all the operations define has logarithmic time complexity
MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CProportionalCoolerThe proportional cooler works the way that the temperature at the next time instant is proportional to the temperature in the previous
MCScheduling.MixedCriticality.CRandomInstanceGeneratorGenerates a list of random MC instances
MCScheduling.GeneticAlgorithm.CRandomTournamentSelectorThe random tournament selection operator implementation
MCScheduling.GeneticAlgorithm.CRankScalingRank scaling may be used to prevent too quick convergence
MCScheduling.GeneticAlgorithm.CRemainderStochasticSamplingThe remainder stochastic sampling selection operator implementation
MCScheduling.ClairvoyantEDF.CriticalListA so-called critical list that is used by the clairvoyant EDF algorithm
MCScheduling.GeneticAlgorithm.CRouletteWheelSelectorThe roulette wheel selection is one of the standard fitness-proportional selection methods
MCScheduling.GeneticAlgorithm.CScrambleMutationThe scramble mutation operator
MCScheduling.GeneticAlgorithm.CSelectorOperatorSerializerA serializer for selector genetic operators
MCScheduling.GeneticAlgorithm.CSigmaScalingSigma scaling may be used to force a constant selection pressure over many generations
MCScheduling.MixedCriticality.SA.CSimulatedAnnealingConfigurationThe CSimulatedAnnealingConfiguration instance stores a configuration of a simulated annealing solver settings
MCScheduling.MixedCriticality.SA.CSimulatedAnnealingSolverThe mixed-criticality scheduling problem solver based on the simulated annealing meta-heuristic method
MCScheduling.MixedCriticality.SA.CSimulatedAnnealingStateThe representation of a solution of an MC instance used by simulated annealing algorithm
MCScheduling.SimulatedAnnealing.CBaseSimulatedAnnealing< T >.CStateThe state keeps information about the energy of the actual state it wraps
MCScheduling.GeneticAlgorithm.CStochasticUniversalSamplingThe stochastic universal sampling selector implementation
MCScheduling.Utils.CUniformRandomThe (uniform random number generator) Mersenne Twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto and Takuji Nishimura
MCScheduling.MixedCriticality.CWorstCaseMixedCriticalityInstanceGeneratorGenerates a random "worst-case" MC instance
MCScheduling.ClairvoyantEDF.Task.ExecutionTimeArrayThe execution times of the task
MCScheduling.GeneticAlgorithm.CPopulation.FittestChromosomeEnumeratorFittestChromosomeEnumerator may be used to enumerate fittest chromosomes of a population in memory efficient way
MCScheduling.GanttViewFormThe GanttViewForm is a window whose client are is filled with a char area that display a Gant diagram of an MC instance
MCScheduling.SimulatedAnnealing.IAcceptorThe interface of the "probability distribution" of a state being accepted according to its distance from the state from which it has been created
MCScheduling.GeneticAlgorithm.IChromosomeInterface providing methods common to all type of chromosomes
MCScheduling.GeneticAlgorithm.IChromosomePoolAn interface for implementation of a chromosome pool
MCScheduling.SimulatedAnnealing.ICloneable< T >Extended support for cloning, which creates a new instance of a generic class with the same value as an existing instance
MCScheduling.SimulatedAnnealing.IComparerThe interface for a comparer of double values
MCScheduling.GeneticAlgorithm.IConfigurationDependentThe genetic algorithm components, i.e
MCScheduling.SimulatedAnnealing.ICoolerThe interface of a temperature cooler that is used to decrease the temperature throughout the optimization process of simulated annealing
MCScheduling.GeneticAlgorithm.ICrossoverOperatorCrossover operator is one of the tools of evolution
MCScheduling.SimulatedAnnealing.IEvaluator< T >Defines an evaluator that is used to evaluate states throughout the optimization process of simulated annealing
MCScheduling.GeneticAlgorithm.IFitnessComparerDefines a fitness comparison method for two chromosomes (objects implementing IChromosome interface)
MCScheduling.GeneticAlgorithm.IFitnessEvaluatorDefines a fitness calculation method
MCScheduling.GeneticAlgorithm.IFitnessScalerDefines a fitness scaling method
MCScheduling.GeneticAlgorithm.IGeneA gene represents a building block from which chromosomes, i.e., solutions to problems are constructed
MCScheduling.GeneticAlgorithm.IGeneDistanceMeasurerAn public interface providing a method to determine the distance between a pair of give genes
MCScheduling.MixedCriticality.IMixedCriticalitySolverAn interface for an algorithn solving mixed-criticality instances
MCScheduling.GeneticAlgorithm.IMutationOperatorMutation operator is one of the tools of evolution
MCScheduling.SimulatedAnnealing.IPerturber< T >The perturber used to change one state into one of its neighbouring state
MCScheduling.GeneticAlgorithm.IRandomizerA pseudo-random number generator used by a genetic algorithms
MCScheduling.GeneticAlgorithm.ISelectionOperatorThe selection operator is responsible for selecting an individual chromosomes from the population that will be then used in the further proceedings of the genetic algorithm: recombination phase, namely
MCScheduling.MainWindowFormThe main window of the application
MCScheduling.ClairvoyantEDF.PriorityQueue< T >A priority queue implementation based on a binary heap structure, which guarantees that all the operations define has logarithmic time complexity
MCScheduling.MixedCriticality.CBaseMixedCriticalitySolver.StatusA status of a solution that is returned as result of the solving algorithm
MCScheduling.ClairvoyantEDF.TaskThe wrapper of mixed-criticality job to be used with priority queues and criticality queue of the clairvoyant EDF algorithm
MCScheduling.Win32Auxiliary class that binds its member methods to the same methods of kernel33.dll library
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