| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/MOGA/src/ |
| H A D | MOGA.cpp | 104 const bool MOGA::_registered_operator_groups =
107 MOGA::RegistryOfOperatorGroups().register_(
112 MOGA::RegistryOfOperatorGroups().register_(
155 MOGA::RegistryOfOperatorGroups( in RegistryOfOperatorGroups()
164 MOGA::ReclaimOptimal( in ReclaimOptimal()
246 MOGA::GetBestDesign( in GetBestDesign()
328 MOGA::GetOperatorGroupRegistry( in GetOperatorGroupRegistry()
336 MOGA::FlushNonOptimal( in FlushNonOptimal()
351 MOGA::GetCurrentSolution( in GetCurrentSolution()
386 MOGA::GetAlgorithmTypeName( in GetAlgorithmTypeName()
[all …]
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/MOGA/include/ |
| H A D | MOGA.hpp | 131 class MOGA;
154 class JEGA_SL_IEDECL MOGA : class
351 MOGA(
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/MOGA/include/inline/ |
| H A D | MOGA.hpp.inl | 9 Inline methods of class MOGA.
13 See notes of MOGA.hpp.
47 * \brief Contains the inline methods of the MOGA class.
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/ |
| H A D | DUPLICATE-unique_roulette_wheel | 8 MOGA or SOGA problems however they are not recommended for use with
9 MOGA. Given that the only two fitness assessors for MOGA are the \c
|
| H A D | DUPLICATE-roulette_wheel | 8 MOGA or SOGA problems however they are not recommended for use with
9 MOGA. Given that the only two fitness assessors for MOGA are the \c
|
| H A D | DUPLICATE-replacement_type | 7 \c roulette_wheel or \c unique_roulette_wheel may be used either with MOGA
9 MOGA. Given that the only two fitness assessors for MOGA are the
|
| H A D | method-moga-postprocessor_type-orthogonal_distance | 4 Note that MOGA and SOGA create additional output files during
12 format "Input1...InputN..Output1...OutputM". If MOGA is used in a
|
| H A D | DUPLICATE-population_size | 12 method-soga-initialization_type-flat_file or MOGA \ref
|
| H A D | method-moga-replacement_type-below_limit-shrinkage_fraction | 4 As of JEGA v2.0, all replacement types are common to both MOGA and
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/FrontEnd/Core/src/ |
| H A D | AlgorithmConfig.cpp | 184 JEGAIFLOG_CF_II_G_F(algType != MOGA && algType != SOGA, this, in SetAlgorithmType()
189 "method.algorithm", algType == MOGA ? "moga" : "soga" in SetAlgorithmType()
273 return algType == "moga" ? MOGA : SOGA; in GetAlgorithmType()
|
| H A D | Driver.cpp | 614 AlgorithmConfig::AlgType algType = AlgorithmConfig::MOGA; in CreateNewAlgorithm()
618 if(algTypeStr == "moga") algType = AlgorithmConfig::MOGA; in CreateNewAlgorithm()
632 if(algType == AlgorithmConfig::MOGA) in CreateNewAlgorithm()
638 theGA = new MOGA(this->_probConfig.GetDesignTarget(), logger); in CreateNewAlgorithm()
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/test/ |
| H A D | dakota_su_mogatest1.in | 16 method_pointer = 'MOGA'
25 id_method = 'MOGA'
|
| H A D | dakota_textbook.in | 33 # method_pointer = 'MOGA' #s10
46 # id_method = 'MOGA' #s10
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/FrontEnd/Managed/include/ |
| H A D | MAlgorithmConfig.hpp | 186 MOGA = JEGA::FrontEnd::AlgorithmConfig::MOGA,
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/unit_test/ |
| H A D | opt_api_traits.cpp | 55 if (methodName == MOGA || methodName == SOGA || in check_variables()
136 …check_variable_consistency( MOGA , std::shared_ptr<TraitsBase>(new JEGATraits()) … in TEUCHOS_UNIT_TEST()
|
| /dports/math/scilab/scilab-6.1.1/scilab/modules/optimization/demos/genetic/ |
| H A D | MOGAdemo.sce | 7 // Demo of the MOGA Genetic Algorithm //
79 // MOGA Algorithm //
83 printf(gettext("%s: optimization starting, please wait ...\n"),"MOGA");
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/docs/users/ch_operators/ |
| H A D | operators.tex | 5 Multi-objective Genetic Algorithm (MOGA) which performs Pareto
67 \emph{MOGA}, the available assessors are the \emph{layer\_rank} and
87 that the only two fitness assessors for MOGA are the
97 to the MOGA and is new to JEGA v2.0. Technically, the step is
100 MOGA, the \emph{radial} niching operator or the \emph{distance}
124 MOGA. As of JEGA v2.0, the same fitness tracker convergers exist
126 with the MOGA. The MOGA converger (\emph{metric\_tracker}) operates
140 There are many controls which can be used for both MOGA and SOGA
440 Also new to JEGA v2.0 is the introduction of the MOGA specific
518 The specification for convergence in a MOGA can either be
[all …]
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/docs/ |
| H A D | JEGA.dox | 156 * Although the primary deliverable of JEGA is the MOGA, a single objective GA
158 * some point in the future in favor of the MOGA which can effectively solve
161 * The remaining discussion of this page is focused on the MOGA and MOP's in
223 * \subsection MOGAs Multi-Objective Genetic Algorithms (MOGA)
226 * solutions is to use a MOGA. A MOGA is a specialized type of genetic
238 * specialized for MOGA has been introduced in JEGA v2. See the
269 * MOGA's do not suffer from the same set of drawbacks.
280 * space may exist in "difficult" regions of the variable space. If a MOGA is
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/ |
| H A D | JEGAOptimizer.cpp | 1175 else if (this->methodName == MOGA) in LoadTheParameterDatabase()
1200 else if (this->methodName == MOGA) in LoadTheParameterDatabase()
1271 else if (this->methodName == MOGA) in LoadTheParameterDatabase()
1364 if(this->methodName == MOGA) in LoadAlgorithmConfig()
1365 algType = AlgorithmConfig::MOGA; in LoadAlgorithmConfig()
1639 if(this->methodName == MOGA) in GetBestSolutions()
1947 if (methodName == MOGA && !this->numFinalSolutions) in JEGAOptimizer()
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/src/OperatorGroups/ |
| H A D | AllOperators.cpp | 227 ABSORB_METHOD(MOGA) in ABSORB_METHOD()
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/TopicMetadata/ |
| H A D | topic-package_jega | 2 optimization methods. The first is a Multi-objective Genetic Algorithm (MOGA)
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/FrontEnd/Core/include/ |
| H A D | AlgorithmConfig.hpp | 185 MOGA, enumerator
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/docs/users/abstract/ |
| H A D | abstract.tex | 6 algorithm (MOGA) for solution to multi-objective optimization
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/docs/users/ch_configuration/ |
| H A D | configuration.tex | 137 run. They are the MOGA and the SOGA. In any given program, JEGA
763 (ex. MOGA \#5).
837 that the only two fitness assessors for MOGA are the
847 to the MOGA and is new to JEGA v2.0. Technically, the step is
850 MOGA, the \emph{radial} niching operator or the \emph{distance}
876 with the MOGA. The MOGA converger (\emph{metric\_tracker}) operates
890 There are many controls which can be used for both MOGA and SOGA
1127 described in the preceding section. There are no MOGA specific
1190 Also new to JEGA v2.0 is the introduction of the MOGA specific
1268 The specification for convergence in a MOGA can either be
[all …]
|
| /dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/JEGA/FrontEnd/Managed/src/ |
| H A D | Managed_JEGA_FE.cpp | 244 aConfig->SetAlgorithmType(MAlgorithmConfig::AlgType::MOGA);
|