- Main Page
- Classes
- Files

- Search for |

- Alphabetical List
- Class List
- Class Hierarchy
- Class Members

# ParadisEO-MOEO Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

MOEO< MOEOObjectiveVector, MOEOFitness, MOEODiversity > | Base class allowing to represent a solution (an individual) for multi-objective optimization
moeoAdditiveEpsilonBinaryMetric< ObjectiveVector > | Additive epsilon binary metric allowing to compare two objective vectors as proposed in Zitzler E., Thiele L., Laumanns M., Fonseca C
moeoArchive< MOEOT > | An archive is a secondary population that stores non-dominated solutions
moeoArchiveObjectiveVectorSavingUpdater< EOT > | This class allows to save the fitnesses of 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
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
moeoCrowdingDistanceDiversityAssignment< MOEOT > | Diversity assignment sheme based on crowding distance proposed in: K
moeoDetTournamentSelect< MOEOT > | Selection strategy that selects ONE individual by deterministic tournament
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
moeoElitistReplacement< MOEOT > | Elitist replacement strategy that consists in keeping the N best individuals
moeoElitistReplacement< MOEOT >::Cmp | This class 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 class is used to compare solutions in order to sort the population
moeoEvalFunc< MOEOT > |
moeoFastNonDominatedSortingFitnessAssignment< MOEOT > | Fitness assignment sheme based on Pareto-dominance count proposed in: N
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
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
moeoIndicatorBasedFitnessAssignment< MOEOT > | Fitness assignment sheme based an Indicator proposed in: E
moeoLS< MOEOT, Type > | Abstract class for local searches applied to multi-objective optimization
moeoMetric | Base class for performance metrics (also known as quality indicators)
moeoNormalizedSolutionVsSolutionBinaryMetric< ObjectiveVector, R > | Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values
moeoNSGAII< MOEOT > | The NSGA-II algorithm as described in: Deb, K., S
moeoObjectiveComparator< MOEOT > | Functor allowing to compare two solutions according to their first objective value, then their second, and so on
moeoObjectiveVector< ObjectiveVectorTraits > | Abstract class allowing to represent a solution in the objective space (phenotypic representation)
moeoObjectiveVectorComparator< ObjectiveVector > | Abstract class allowing to compare 2 objective vectors
moeoObjectiveVectorDouble< ObjectiveVectorTraits > | This class allows to represent a solution in the objective space (phenotypic representation) by a std::vector of doubles, i.e
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
moeoRealVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity > | This class is an implementationeo 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
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
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)