MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
Classes
Package MCScheduling.GeneticAlgorithm

Contains the implementation of a (generic) genetic algorithm that may be used to solve different kind of optimization problems. More...

Classes

class  CAlternatingPositionCrossover
 The alternating position crossover operator implementation. More...
class  CBaseBreeder
 A breeder evolves a population by performing genetic operations defined in the specified configuration of the genetic algorithm. More...
class  CBaseCrossoverOperator
 A base class for implementation of a crossover genetic operator. More...
class  CBaseGeneticAlgorithm
 A base class for implementation of a genetic algorithm. More...
class  CBaseMutationOperator
 A base class for implementation of a mutation genetic operator. More...
class  CBaseSelectionOperator
 A base class for implementation of a natural selector. More...
class  CBoltzmannScaling
 In case one wants the selection preasure to vary during the evolution he uses Bolzmann scaling. More...
class  CChromosomePool
 The basic implementation of the IChromosomePool interface. More...
class  CConfiguration
 The configuration represents a setting of a genetic algorithm, i.e. More...
class  CCrossoverOperatorSerializer
 A serializer for crossover operators. More...
class  CCycleCrossover
 The cycle crossover operator implementation. More...
class  CDefaultBreeder
 A default implementation of IBreeder interface. More...
class  CDeterministicSampling
 The deterministic sampling selection operator implementation. More...
class  CDisplacedInversionMutation
 The displaced inversion mutation operator. More...
class  CDisplacementMutation
 The displacement mutation operator. More...
class  CExchangeMutation
 The exchange mutation operator. More...
class  CFitnessEvaluatorSerializer
 The serializer for the implemented finess evalutors. More...
class  CFitnessScalerSerializer
 The serializer for the implemented finess scalers. More...
class  CGeneticOperator
 The base public class for implementation of a genetic operator. More...
class  CGreedyCrossover
 The greedy crossover operator implementation. More...
class  CHeuristicCrossover
 The heuristic crossover operator implementation. More...
class  CInsertionMutation
 The insertion mutation operator. More...
class  CInversionMutation
 The inversion mutation operator. More...
class  CMersenneTwister
 The Mersenne Twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto and Takuji Nishimura. More...
class  CMutationOperatorSerializer
 A serializer for mutation operators. More...
class  CNullFitnessScaler
 A fitness scaler that leaves the fitness value as they are - does not scale. More...
class  COrderBasedCrossover
 The order-based crossover operator implementation. More...
class  COrderCrossover
 The order crossover operator implementation. More...
class  CPartiallyMappedCrossover
 The partially mapped crossover operator implementation. More...
class  CPopulation
 A population consist of a individual chromosomes that forms potential solutions to a problem. More...
class  CPositionBasedCrossover
 The postion-based crossover operator implementation. More...
class  CPriorityQueue
 A priority queue implementation based on a binary heap structure, which guarantees that all the operations define has logarithmic time complexity. More...
class  CRandomTournamentSelector
 The random tournament selection operator implementation. More...
class  CRankScaling
 Rank scaling may be used to prevent too quick convergence. More...
class  CRemainderStochasticSampling
 The remainder stochastic sampling selection operator implementation. More...
class  CRouletteWheelSelector
 The roulette wheel selection is one of the standard fitness-proportional selection methods. More...
class  CScrambleMutation
 The scramble mutation operator. More...
class  CSelectorOperatorSerializer
 A serializer for selector genetic operators. More...
class  CSigmaScaling
 Sigma scaling may be used to force a constant selection pressure over many generations. More...
class  CStochasticUniversalSampling
 The stochastic universal sampling selector implementation. More...
interface  IChromosome
 Interface providing methods common to all type of chromosomes. More...
interface  IChromosomePool
 An interface for implementation of a chromosome pool. More...
interface  IConfigurationDependent
 The genetic algorithm components, i.e. More...
interface  ICrossoverOperator
 Crossover operator is one of the tools of evolution. More...
interface  IFitnessComparer
 Defines a fitness comparison method for two chromosomes (objects implementing IChromosome interface). More...
interface  IFitnessEvaluator
 Defines a fitness calculation method. More...
interface  IFitnessScaler
 Defines a fitness scaling method. More...
interface  IGene
 A gene represents a building block from which chromosomes, i.e., solutions to problems are constructed. More...
interface  IGeneDistanceMeasurer
 An public interface providing a method to determine the distance between a pair of give genes. More...
interface  IMutationOperator
 Mutation operator is one of the tools of evolution. More...
interface  IRandomizer
 A pseudo-random number generator used by a genetic algorithms. More...
interface  ISelectionOperator
 The 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. More...

Detailed Description

Contains the implementation of a (generic) genetic algorithm that may be used to solve different kind of optimization problems.

It contains a lot of permutation-based crossover and mutation operators, fitness scaling methods, and methods of selection.

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