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/* -*- 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/.
*
*/
#ifndef INCLUDED_SCCOMP_SOURCE_DIFFERENTIALEVOLUTION_HXX
#define INCLUDED_SCCOMP_SOURCE_DIFFERENTIALEVOLUTION_HXX
#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;
}
};
#endif
/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
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