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Grid/extras/Hadrons/GeneticScheduler.hpp

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/*******************************************************************************
Grid physics library, www.github.com/paboyle/Grid
Source file: programs/Hadrons/GeneticScheduler.hpp
Copyright (C) 2016
Author: Antonin Portelli <antonin.portelli@me.com>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
See the full license in the file "LICENSE" in the top level distribution
directory.
*******************************************************************************/
#ifndef Hadrons_GeneticScheduler_hpp_
#define Hadrons_GeneticScheduler_hpp_
#include <Grid/Hadrons/Global.hpp>
#include <Grid/Hadrons/Graph.hpp>
BEGIN_HADRONS_NAMESPACE
/******************************************************************************
* Scheduler based on a genetic algorithm *
******************************************************************************/
template <typename T>
class GeneticScheduler
{
public:
typedef std::vector<T> Gene;
typedef std::pair<Gene *, Gene *> GenePair;
typedef std::function<int(const Gene &)> ObjFunc;
struct Parameters
{
double mutationRate;
unsigned int popSize, seed;
};
public:
// constructor
GeneticScheduler(Graph<T> &graph, const ObjFunc &func,
const Parameters &par);
// destructor
virtual ~GeneticScheduler(void) = default;
// access
const Gene & getMinSchedule(void);
int getMinValue(void);
// breed a new generation
void nextGeneration(void);
// heuristic benchmarks
void benchmarkCrossover(const unsigned int nIt);
// print population
friend std::ostream & operator<<(std::ostream &out,
const GeneticScheduler<T> &s)
{
out << "[";
for (auto &p: s.population_)
{
out << p.first << ", ";
}
out << "\b\b]";
return out;
}
private:
// evolution steps
void initPopulation(void);
void doCrossover(void);
void doMutation(void);
// genetic operators
GenePair selectPair(void);
void crossover(Gene &c1, Gene &c2, const Gene &p1, const Gene &p2);
void mutation(Gene &m, const Gene &c);
private:
Graph<T> &graph_;
const ObjFunc &func_;
const Parameters par_;
std::multimap<int, Gene> population_;
std::mt19937 gen_;
};
/******************************************************************************
* template implementation *
******************************************************************************/
// constructor /////////////////////////////////////////////////////////////////
template <typename T>
GeneticScheduler<T>::GeneticScheduler(Graph<T> &graph, const ObjFunc &func,
const Parameters &par)
: graph_(graph)
, func_(func)
, par_(par)
{
gen_.seed(par_.seed);
}
// access //////////////////////////////////////////////////////////////////////
template <typename T>
const typename GeneticScheduler<T>::Gene &
GeneticScheduler<T>::getMinSchedule(void)
{
return population_.begin()->second;
}
template <typename T>
int GeneticScheduler<T>::getMinValue(void)
{
return population_.begin()->first;
}
// breed a new generation //////////////////////////////////////////////////////
template <typename T>
void GeneticScheduler<T>::nextGeneration(void)
{
// random initialization of the population if necessary
if (population_.size() != par_.popSize)
{
initPopulation();
}
LOG(Debug) << "Starting population:\n" << *this << std::endl;
// random mutations
PARALLEL_FOR_LOOP
for (unsigned int i = 0; i < par_.popSize; ++i)
{
doMutation();
}
LOG(Debug) << "After mutations:\n" << *this << std::endl;
// mating
PARALLEL_FOR_LOOP
for (unsigned int i = 0; i < par_.popSize/2; ++i)
{
doCrossover();
}
LOG(Debug) << "After mating:\n" << *this << std::endl;
// grim reaper
auto it = population_.begin();
std::advance(it, par_.popSize);
population_.erase(it, population_.end());
LOG(Debug) << "After grim reaper:\n" << *this << std::endl;
}
// evolution steps /////////////////////////////////////////////////////////////
template <typename T>
void GeneticScheduler<T>::initPopulation(void)
{
population_.clear();
for (unsigned int i = 0; i < par_.popSize; ++i)
{
auto p = graph_.topoSort(gen_);
population_.emplace(func_(p), p);
}
}
template <typename T>
void GeneticScheduler<T>::doCrossover(void)
{
auto p = selectPair();
Gene &p1 = *(p.first), &p2 = *(p.second);
Gene c1, c2;
crossover(c1, c2, p1, p2);
PARALLEL_CRITICAL
{
population_.emplace(func_(c1), c1);
population_.emplace(func_(c2), c2);
}
}
template <typename T>
void GeneticScheduler<T>::doMutation(void)
{
std::uniform_real_distribution<double> mdis(0., 1.);
std::uniform_int_distribution<unsigned int> pdis(0, population_.size() - 1);
if (mdis(gen_) < par_.mutationRate)
{
Gene m;
auto it = population_.begin();
std::advance(it, pdis(gen_));
mutation(m, it->second);
PARALLEL_CRITICAL
{
population_.emplace(func_(m), m);
}
}
}
// genetic operators ///////////////////////////////////////////////////////////
template <typename T>
typename GeneticScheduler<T>::GenePair GeneticScheduler<T>::selectPair(void)
{
std::vector<double> prob;
unsigned int ind;
Gene *p1, *p2;
for (auto &c: population_)
{
prob.push_back(1./c.first);
}
do
{
double probCpy;
std::discrete_distribution<unsigned int> dis1(prob.begin(), prob.end());
auto rIt = population_.begin();
ind = dis1(gen_);
std::advance(rIt, ind);
p1 = &(rIt->second);
probCpy = prob[ind];
prob[ind] = 0.;
std::discrete_distribution<unsigned int> dis2(prob.begin(), prob.end());
rIt = population_.begin();
std::advance(rIt, dis2(gen_));
p2 = &(rIt->second);
prob[ind] = probCpy;
} while (p1 == p2);
return std::make_pair(p1, p2);
}
template <typename T>
void GeneticScheduler<T>::crossover(Gene &c1, Gene &c2, const Gene &p1,
const Gene &p2)
{
Gene buf;
std::uniform_int_distribution<unsigned int> dis(0, p1.size() - 1);
unsigned int cut = dis(gen_);
c1.clear();
buf = p2;
for (unsigned int i = 0; i < cut; ++i)
{
c1.push_back(p1[i]);
buf.erase(std::find(buf.begin(), buf.end(), p1[i]));
}
for (unsigned int i = 0; i < buf.size(); ++i)
{
c1.push_back(buf[i]);
}
c2.clear();
buf = p2;
for (unsigned int i = cut; i < p1.size(); ++i)
{
buf.erase(std::find(buf.begin(), buf.end(), p1[i]));
}
for (unsigned int i = 0; i < buf.size(); ++i)
{
c2.push_back(buf[i]);
}
for (unsigned int i = cut; i < p1.size(); ++i)
{
c2.push_back(p1[i]);
}
}
template <typename T>
void GeneticScheduler<T>::mutation(Gene &m, const Gene &c)
{
Gene buf;
std::uniform_int_distribution<unsigned int> dis(0, c.size() - 1);
unsigned int cut = dis(gen_);
Graph<T> g1 = graph_, g2 = graph_;
for (unsigned int i = 0; i < cut; ++i)
{
g1.removeVertex(c[i]);
}
for (unsigned int i = cut; i < c.size(); ++i)
{
g2.removeVertex(c[i]);
}
if (g1.size() > 0)
{
buf = g1.topoSort(gen_);
}
if (g2.size() > 0)
{
m = g2.topoSort(gen_);
}
for (unsigned int i = cut; i < c.size(); ++i)
{
m.push_back(buf[i - cut]);
}
}
template <typename T>
void GeneticScheduler<T>::benchmarkCrossover(const unsigned int nIt)
{
Gene p1, p2, c1, c2;
double neg = 0., eq = 0., pos = 0., total;
int improvement;
LOG(Message) << "Benchmarking crossover..." << std::endl;
for (unsigned int i = 0; i < nIt; ++i)
{
p1 = graph_.topoSort(gen_);
p2 = graph_.topoSort(gen_);
crossover(c1, c2, p1, p2);
improvement = (func_(c1) + func_(c2) - func_(p1) - func_(p2))/2;
if (improvement < 0) neg++; else if (improvement == 0) eq++; else pos++;
}
total = neg + eq + pos;
LOG(Message) << " -: " << neg/total << " =: " << eq/total
<< " +: " << pos/total << std::endl;
}
END_HADRONS_NAMESPACE
#endif // Hadrons_GeneticScheduler_hpp_
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