| FlowShop | Structure of the genotype for the flow-shop scheduling problem: a vector of unsigned int int |
| FlowShopBenchmarkParser | Class to handle parameters of a flow-shop instance from a benchmark file |
| FlowShopEval | Evaluation of the objective vector a (multi-objective) FlowShop object |
| FlowShopObjectiveVectorTraits | Definition of the objective vector traits for multi-objective flow-shop problems |
| FlowShopOpCrossoverQuad | Quadratic crossover operator for flow-shop (modify the both genotypes) |
| MOEO< MOEOObjectiveVector, MOEOFitness, MOEODiversity > | Base class allowing to represent a solution (an individual) for multi-objective optimization |
| moeoAchievementFitnessAssignment< MOEOT > | Fitness assignment sheme based on the achievement scalarizing function propozed by Wiersbicki (1980) |
| moeoAdditiveEpsilonBinaryMetric< ObjectiveVector > | Additive epsilon binary metric allowing to compare two objective vectors as proposed in Zitzler E., Thiele L., Laumanns M., Fonseca C |
| moeoAggregativeComparator< MOEOT > | Functor allowing to compare two solutions according to their fitness and diversity values, each according to its aggregative value |
| moeoAlgo | Abstract class for multi-objective algorithms |
| moeoArchive< MOEOT > | An archive is a secondary population that stores non-dominated solutions |
| moeoArchiveObjectiveVectorSavingUpdater< MOEOT > | This class allows to save the objective vectors of the solutions contained in an archive into a file at each generation |
| moeoArchiveUpdater< MOEOT > | This class allows to update the archive at each generation with newly found non-dominated solutions |
| moeoBinaryIndicatorBasedFitnessAssignment< MOEOT > | MoeoIndicatorBasedFitnessAssignment for binary indicators |
| moeoBinaryMetric< A1, A2, R > | Base class for binary metrics |
| moeoBinaryMetricSavingUpdater< MOEOT > | This class allows to save the progression of a binary metric comparing the objective vectors of the current population (or archive) with the objective vectors of the population (or archive) of the generation (n-1) into a file |
| moeoBitVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity > | This class is an implementationeo of a simple bit-valued moeoVector |
| moeoCombinedLS< MOEOT, Type > | This class allows to embed a set of local searches that are sequentially applied, and so working and updating the same archive of non-dominated solutions |
| moeoComparator< MOEOT > | Functor allowing to compare two solutions |
| moeoContributionMetric< ObjectiveVector > | The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc |
| moeoConvertPopToObjectiveVectors< MOEOT, ObjectiveVector > | Functor allowing to get a vector of objective vectors from a population |
| moeoCriterionBasedFitnessAssignment< MOEOT > | MoeoCriterionBasedFitnessAssignment is a moeoFitnessAssignment for criterion-based strategies |
| moeoCrowdingDiversityAssignment< MOEOT > | Diversity assignment sheme based on crowding proposed in: K |
| moeoDetTournamentSelect< MOEOT > | Selection strategy that selects ONE individual by deterministic tournament |
| moeoDistance< MOEOT, Type > | The base class for distance computation |
| moeoDistanceMatrix< MOEOT, Type > | A matrix to compute distances between every pair of individuals contained in a population |
| moeoDiversityAssignment< MOEOT > | Functor that sets the diversity values of a whole population |
| moeoDiversityThenFitnessComparator< MOEOT > | Functor allowing to compare two solutions according to their diversity values, then according to their fitness values |
| moeoDummyDiversityAssignment< MOEOT > | MoeoDummyDiversityAssignment is a moeoDiversityAssignment that gives the value '0' as the individual's diversity for a whole population if it is invalid |
| moeoDummyFitnessAssignment< MOEOT > | MoeoDummyFitnessAssignment is a moeoFitnessAssignment that gives the value '0' as the individual's fitness for a whole population if it is invalid |
| moeoEA< MOEOT > | Abstract class for multi-objective evolutionary algorithms |
| moeoEasyEA< MOEOT > | An easy class to design multi-objective evolutionary algorithms |
| moeoEasyEA< MOEOT >::eoDummyEval | Dummy eval |
| moeoEasyEA< MOEOT >::eoDummySelect | Dummy select |
| moeoEasyEA< MOEOT >::eoDummyTransform | Dummy transform |
| moeoElitistReplacement< MOEOT > | Elitist replacement strategy that consists in keeping the N best individuals |
| moeoElitistReplacement< MOEOT >::Cmp | This object is used to compare solutions in order to sort the population |
| moeoEntropyMetric< ObjectiveVector > | The entropy gives an idea of the diversity of a Pareto set relatively to another (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc |
| moeoEnvironmentalReplacement< MOEOT > | Environmental replacement strategy that consists in keeping the N best individuals by deleting individuals 1 by 1 and by updating the fitness and diversity values after each deletion |
| moeoEnvironmentalReplacement< MOEOT >::Cmp | This object is used to compare solutions in order to sort the population |
| moeoEuclideanDistance< MOEOT > | A class allowing to compute an euclidian distance between two solutions in the objective space with normalized objective values (i.e |
| moeoEvalFunc< MOEOT > | |
| moeoExpBinaryIndicatorBasedFitnessAssignment< MOEOT > | Fitness assignment sheme based on an indicator proposed in: E |
| moeoFastNonDominatedSortingFitnessAssignment< MOEOT > | Fitness assignment sheme based on Pareto-dominance count proposed in: N |
| moeoFastNonDominatedSortingFitnessAssignment< MOEOT >::ObjectiveComparator | Functor allowing to compare two solutions according to their first objective value, then their second, and so on |
| moeoFitnessAssignment< MOEOT > | Functor that sets the fitness values of a whole population |
| moeoFitnessThenDiversityComparator< MOEOT > | Functor allowing to compare two solutions according to their fitness values, then according to their diversity values |
| moeoFrontByFrontCrowdingDiversityAssignment< MOEOT > | Diversity assignment sheme based on crowding proposed in: K |
| moeoFrontByFrontSharingDiversityAssignment< MOEOT > | Sharing assignment scheme on the way it is used in NSGA |
| moeoGDominanceObjectiveVectorComparator< ObjectiveVector > | This functor class allows to compare 2 objective vectors according to g-dominance |
| moeoGenerationalReplacement< MOEOT > | Generational replacement: only the new individuals are preserved |
| moeoHybridLS< MOEOT > | This class allows to apply a multi-objective local search to a number of selected individuals contained in the archive at every generation until a stopping criteria is verified |
| moeoHypervolumeBinaryMetric< ObjectiveVector > | Hypervolume binary metric allowing to compare two objective vectors as proposed in Zitzler E., Künzli S |
| moeoIBEA< MOEOT > | IBEA (Indicator-Based Evolutionary Algorithm) as described in: E |
| moeoIndicatorBasedFitnessAssignment< MOEOT > | MoeoIndicatorBasedFitnessAssignment is a moeoFitnessAssignment for Indicator-based strategies |
| moeoLS< MOEOT, Type > | Abstract class for local searches applied to multi-objective optimization |
| moeoManhattanDistance< MOEOT > | A class allowing to compute the Manhattan distance between two solutions in the objective space normalized objective values (i.e |
| moeoMetric | Base class for performance metrics (also known as quality indicators) |
| moeoNormalizedDistance< MOEOT, Type > | The base class for double distance computation with normalized objective values (i.e |
| moeoNormalizedSolutionVsSolutionBinaryMetric< ObjectiveVector, R > | Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values |
| moeoNSGA< MOEOT > | NSGA (Non-dominated Sorting Genetic Algorithm) as described in: N |
| moeoNSGAII< MOEOT > | NSGA-II (Non-dominated Sorting Genetic Algorithm II) as described in: Deb, K., S |
| moeoObjectiveObjectiveVectorComparator< ObjectiveVector > | Functor allowing to compare two objective vectors according to their first objective value, then their second, and so on |
| moeoObjectiveVector< ObjectiveVectorTraits, ObjectiveVectorType > | Abstract class allowing to represent a solution in the objective space (phenotypic representation) |
| moeoObjectiveVectorComparator< ObjectiveVector > | Abstract class allowing to compare 2 objective vectors |
| moeoObjectiveVectorTraits | A traits class for moeoObjectiveVector to specify the number of objectives and which ones have to be minimized or maximized |
| moeoOneObjectiveComparator< MOEOT > | Functor allowing to compare two solutions according to one objective |
| moeoParetoBasedFitnessAssignment< MOEOT > | MoeoParetoBasedFitnessAssignment is a moeoFitnessAssignment for Pareto-based strategies |
| moeoParetoObjectiveVectorComparator< ObjectiveVector > | This functor class allows to compare 2 objective vectors according to Pareto dominance |
| moeoRandomSelect< MOEOT > | Selection strategy that selects only one element randomly from a whole population |
| moeoRealObjectiveVector< ObjectiveVectorTraits > | This class allows to represent a solution in the objective space (phenotypic representation) by a std::vector of real values, i.e |
| moeoRealVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity > | This class is an implementation of a simple double-valued moeoVector |
| moeoReplacement< MOEOT > | Replacement strategy for multi-objective optimization |
| moeoRouletteSelect< MOEOT > | Selection strategy that selects ONE individual by using roulette wheel process |
| moeoScalarFitnessAssignment< MOEOT > | MoeoScalarFitnessAssignment is a moeoFitnessAssignment for scalar strategies |
| moeoSelectFromPopAndArch< MOEOT > | Elitist selection process that consists in choosing individuals in the archive as well as in the current population |
| moeoSelectOne< MOEOT > | Selection strategy for multi-objective optimization that selects only one element from a whole population |
| moeoSharingDiversityAssignment< MOEOT > | Sharing assignment scheme originally porposed by: D |
| moeoSolutionUnaryMetric< ObjectiveVector, R > | Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector |
| moeoSolutionVsSolutionBinaryMetric< ObjectiveVector, R > | Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors |
| moeoStochTournamentSelect< MOEOT > | Selection strategy that selects ONE individual by stochastic tournament |
| moeoUnaryIndicatorBasedFitnessAssignment< MOEOT > | MoeoIndicatorBasedFitnessAssignment for unary indicators |
| moeoUnaryMetric< A, R > | Base class for unary metrics |
| moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType > | Base class for fixed length chromosomes, just derives from MOEO and std::vector and redirects the smaller than operator to MOEO (objective vector based comparison) |
| moeoVectorUnaryMetric< ObjectiveVector, R > | Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors) |
| moeoVectorVsVectorBinaryMetric< ObjectiveVector, R > | Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors) |
| ObjectiveVectorTraits | |
| Sch1 | |
| Sch1Eval | |
| Sch1ObjectiveVectorTraits | |
| Solution | |
| TestEval | |
