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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/.
+ *
+ */
+
+#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: */