ElitistCMA.h
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief Implements the most recent version of the elitist CMA-ES.
6  *
7  * The algorithm is based on
8  *
9  * C. Igel, T. Suttorp, and N. Hansen. A Computational Efficient
10  * Covariance Matrix Update and a (1+1)-CMA for Evolution
11  * Strategies. In Proceedings of the Genetic and Evolutionary
12  * Computation Conference (GECCO 2006), pp. 453-460, ACM Press, 2006
13  *
14  * D. V. Arnold and N. Hansen: Active covariance matrix adaptation for
15  * the (1+1)-CMA-ES. In Proceedings of the Genetic and Evolutionary
16  * Computation Conference (GECCO 2010): pp 385-392, ACM Press 2010
17  *
18  *
19  * \author O. Krause T.Voss
20  * \date 2014
21  *
22  *
23  * \par Copyright 1995-2017 Shark Development Team
24  *
25  * <BR><HR>
26  * This file is part of Shark.
27  * <http://shark-ml.org/>
28  *
29  * Shark is free software: you can redistribute it and/or modify
30  * it under the terms of the GNU Lesser General Public License as published
31  * by the Free Software Foundation, either version 3 of the License, or
32  * (at your option) any later version.
33  *
34  * Shark is distributed in the hope that it will be useful,
35  * but WITHOUT ANY WARRANTY; without even the implied warranty of
36  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
37  * GNU Lesser General Public License for more details.
38  *
39  * You should have received a copy of the GNU Lesser General Public License
40  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
41  *
42  */
43 //===========================================================================
44 
45 
46 #ifndef SHARK_ALGORITHMS_DIRECTSEARCH_ELITIST_CMA_H
47 #define SHARK_ALGORITHMS_DIRECTSEARCH_ELITIST_CMA_H
48 
49 #include <shark/Core/DLLSupport.h>
50 #include <shark/Algorithms/AbstractSingleObjectiveOptimizer.h>
51 #include <shark/Algorithms/DirectSearch/CMA/CMAIndividual.h>
52 #include <shark/Algorithms/DirectSearch/Operators/Evaluation/PenalizingEvaluator.h>
53 
54 namespace shark {
55 
56 /**
57 * \brief Implements the elitist CMA-ES.
58 *
59 * The algorithm is based on
60 *
61 * C. Igel, T. Suttorp, and N. Hansen. A Computational Efficient
62 * Covariance Matrix Update and a (1+1)-CMA for Evolution
63 * Strategies. In Proceedings of the Genetic and Evolutionary
64 * Computation Conference (GECCO 2006), pp. 453-460, ACM Press, 2006
65 *
66 * D. V. Arnold and N. Hansen: Active covariance matrix adaptation for
67 * the (1+1)-CMA-ES. In Proceedings of the Genetic and Evolutionary
68 */
69 class ElitistCMA : public AbstractSingleObjectiveOptimizer<RealVector >{
70 public:
71 
72  SHARK_EXPORT_SYMBOL ElitistCMA(random::rng_type& rng = random::globalRng);
73 
74  /// \brief From INameable: return the class name.
75  std::string name() const
76  { return "ElitistCMA"; }
77 
78  SHARK_EXPORT_SYMBOL void read( InArchive & archive );
79 
80  SHARK_EXPORT_SYMBOL void write( OutArchive & archive ) const;
81 
82  using AbstractSingleObjectiveOptimizer<RealVector >::init;
83 
84  /// \brief Initializes the algorithm for the supplied objective function.
85  SHARK_EXPORT_SYMBOL void init( ObjectiveFunctionType const& function, SearchPointType const& p);
86 
87  ///\brief Executes one iteration of the algorithm.
88  SHARK_EXPORT_SYMBOL void step(ObjectiveFunctionType const& function);
89 
90  /// \brief Returns true when the active update is used (default true).
91  bool activeUpdate()const{
92  return m_activeUpdate;
93  }
94  /// \brief Setter function to enable active update. Returns true when the active update is used (default true).
95  bool& activeUpdate(){
96  return m_activeUpdate;
97  }
98 
99  /// \brief Returns the penalty factor for an individual that is outside the feasible area.
100  ///
101  /// The value is multiplied with the distance to the nearest feasible point.
102  double constrainedPenaltyFactor()const{
103  return m_evaluator.m_penaltyFactor;
104  }
105 
106  /// \brief Returns a reference to the penalty factor for an individual that is outside the feasible area.
107  ///
108  /// The value is multiplied with the distance to the nearest feasible point.
109  double& constrainedPenaltyFactor(){
110  return m_evaluator.m_penaltyFactor;
111  }
112 
113  /// \brief Returns the current step length
114  double sigma()const{
115  return m_individual.chromosome().m_stepSize;
116  }
117 
118  /// \brief Returns the current step length
119  double& sigma(){
120  return m_individual.chromosome().m_stepSize;
121  }
122 
123 private:
124  CMAIndividual<double> m_individual;///< Individual holding strategy parameter. usd as parent and offspring
125  PenalizingEvaluator m_evaluator;///< evaluates the fitness of the individual and handles constraints
126  std::vector<double> m_ancestralFitness; ///< stores the last k fitness values (by default 5).
127  bool m_activeUpdate;///< Should bad individuals be actively purged from the strategy?
128 
129  random::rng_type* mpe_rng;///< the internal random number generator
130 };
131 }
132 
133 #endif