1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
|
/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
* This file is part of the LibreOffice project.
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
*/
#pragma once
#include <vector>
#include <random>
#include <limits>
struct Individual
{
std::vector<double> mVariables;
};
template <typename DataProvider> class DifferentialEvolutionAlgorithm
{
static constexpr double mnDifferentialWeight = 0.5; // [0, 2]
static constexpr double mnCrossoverProbability = 0.9; // [0, 1]
static constexpr double constAcceptedPrecision = 0.000000001;
DataProvider& mrDataProvider;
size_t mnPopulationSize;
std::vector<Individual> maPopulation;
std::random_device maRandomDevice;
std::mt19937 maGenerator;
size_t mnDimensionality;
std::uniform_int_distribution<> maRandomPopulation;
std::uniform_int_distribution<> maRandomDimensionality;
std::uniform_real_distribution<> maRandom01;
Individual maBestCandidate;
double mfBestFitness;
int mnGeneration;
int mnLastChange;
public:
DifferentialEvolutionAlgorithm(DataProvider& rDataProvider, size_t nPopulationSize)
: mrDataProvider(rDataProvider)
, mnPopulationSize(nPopulationSize)
, maGenerator(maRandomDevice())
, mnDimensionality(mrDataProvider.getDimensionality())
, maRandomPopulation(0, mnPopulationSize - 1)
, maRandomDimensionality(0, mnDimensionality - 1)
, maRandom01(0.0, 1.0)
, mfBestFitness(std::numeric_limits<double>::lowest())
, mnGeneration(0)
, mnLastChange(0)
{
}
std::vector<double> const& getResult() { return maBestCandidate.mVariables; }
int getGeneration() { return mnGeneration; }
int getLastChange() { return mnLastChange; }
void initialize()
{
mnGeneration = 0;
mnLastChange = 0;
maPopulation.clear();
maBestCandidate.mVariables.clear();
// Initialize population with individuals that have been initialized with uniform random
// noise
// uniform noise means random value inside your search space
maPopulation.reserve(mnPopulationSize);
for (size_t i = 0; i < mnPopulationSize; ++i)
{
maPopulation.emplace_back();
Individual& rIndividual = maPopulation.back();
mrDataProvider.initializeVariables(rIndividual.mVariables, maGenerator);
}
}
// Calculate one generation
bool next()
{
bool bBestChanged = false;
for (size_t agentIndex = 0; agentIndex < mnPopulationSize; ++agentIndex)
{
// calculate new candidate solution
// pick random point from population
size_t x = agentIndex; // randomPopulation(generator);
size_t a, b, c;
// create a copy of chosen random agent in population
Individual& rOriginal = maPopulation[x];
Individual aCandidate(rOriginal);
// pick three different random points from population
do
{
a = maRandomPopulation(maGenerator);
} while (a == x);
do
{
b = maRandomPopulation(maGenerator);
} while (b == x || b == a);
do
{
c = maRandomPopulation(maGenerator);
} while (c == x || c == a || c == b);
size_t randomIndex = maRandomDimensionality(maGenerator);
for (size_t index = 0; index < mnDimensionality; ++index)
{
double randomCrossoverProbability = maRandom01(maGenerator);
if (index == randomIndex || randomCrossoverProbability < mnCrossoverProbability)
{
double fVarA = maPopulation[a].mVariables[index];
double fVarB = maPopulation[b].mVariables[index];
double fVarC = maPopulation[c].mVariables[index];
double fNewValue = fVarA + mnDifferentialWeight * (fVarB - fVarC);
fNewValue = mrDataProvider.boundVariable(index, fNewValue);
aCandidate.mVariables[index] = fNewValue;
}
}
double fCandidateFitness = mrDataProvider.calculateFitness(aCandidate.mVariables);
// see if is better than original, if so replace
if (fCandidateFitness > mrDataProvider.calculateFitness(rOriginal.mVariables))
{
maPopulation[x] = aCandidate;
if (fCandidateFitness > mfBestFitness)
{
if (std::abs(fCandidateFitness - mfBestFitness) > constAcceptedPrecision)
{
bBestChanged = true;
mnLastChange = mnGeneration;
}
mfBestFitness = fCandidateFitness;
maBestCandidate = maPopulation[x];
}
}
}
mnGeneration++;
return bBestChanged;
}
};
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
|
