CIGTAB1.h
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief Multi-objective optimization benchmark function CIGTAB 1.
6  *
7  * The function is described in
8  *
9  * Christian Igel, Nikolaus Hansen, and Stefan Roth.
10  * Covariance Matrix Adaptation for Multi-objective Optimization.
11  * Evolutionary Computation 15(1), pp. 1-28, 2007
12  *
13  *
14  *
15  * \author -
16  * \date -
17  *
18  *
19  * \par Copyright 1995-2017 Shark Development Team
20  *
21  * <BR><HR>
22  * This file is part of Shark.
23  * <http://shark-ml.org/>
24  *
25  * Shark is free software: you can redistribute it and/or modify
26  * it under the terms of the GNU Lesser General Public License as published
27  * by the Free Software Foundation, either version 3 of the License, or
28  * (at your option) any later version.
29  *
30  * Shark is distributed in the hope that it will be useful,
31  * but WITHOUT ANY WARRANTY; without even the implied warranty of
32  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
33  * GNU Lesser General Public License for more details.
34  *
35  * You should have received a copy of the GNU Lesser General Public License
36  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
37  *
38  */
39 //===========================================================================
40 #ifndef SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_CIGTAB1_H
41 #define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_CIGTAB1_H
42 
45 #include <shark/LinAlg/rotations.h>
46 
47 
48 namespace shark {
49 /*! \brief Multi-objective optimization benchmark function CIGTAB 1.
50 *
51 * The function is described in
52 *
53 * Christian Igel, Nikolaus Hansen, and Stefan Roth.
54 * Covariance Matrix Adaptation for Multi-objective Optimization.
55 * Evolutionary Computation 15(1), pp. 1-28, 2007
56 */
58 
59  CIGTAB1(std::size_t numberOfVariables = 5) : m_a( 1E6 ) {
61  m_numberOfVariables = numberOfVariables;
62  }
63 
64  /// \brief From INameable: return the class name.
65  std::string name() const
66  { return "CIGTAB1"; }
67 
68  std::size_t numberOfObjectives()const{
69  return 2;
70  }
71 
72  std::size_t numberOfVariables()const{
73  return m_numberOfVariables;
74  }
75 
77  return true;
78  }
79 
80  /// \brief Adjusts the number of variables if the function is scalable.
81  /// \param [in] numberOfVariables The new dimension.
83  m_numberOfVariables = numberOfVariables;
84  }
85 
86  void init() {
87  m_rotationMatrix = blas::randomRotationMatrix(*mep_rng, m_numberOfVariables);
88  }
89 
90  ResultType eval( const SearchPointType & x ) const {
92 
93  ResultType value(2);
94 
95  ResultType y = prod( m_rotationMatrix, x );
96  double result = sqr( y(0) ) + sqr( m_a ) * sqr( y( numberOfVariables() - 1 ) );
97 
98  for (unsigned i = 1; i < numberOfVariables() - 1; i++) {
99  result += m_a * sqr( y( i ) );
100  }
101 
102  value[0] = result / ( sqr(m_a) * numberOfVariables() );
103 
104  result = sqr(y( 0 ) - 2) + sqr(m_a) * sqr(y(numberOfVariables()-1) - 2);
105 
106  for (unsigned i = 1; i < numberOfVariables() - 1; i++) {
107  result += m_a * sqr(y( i ) - 2);
108  }
109 
110  value[1] = result / ( sqr(m_a) * numberOfVariables() );
111 
112  return value;
113  }
114 
116  RealVector x(m_numberOfVariables);
117 
118  for (std::size_t i = 0; i < x.size(); i++) {
119  x(i) = random::uni(*mep_rng, -10.0, 10.0);
120  }
121  return x;
122  }
123 private:
124  double m_a;
125  RealMatrix m_rotationMatrix;
126  std::size_t m_numberOfVariables;
127 };
128 }
129 #endif