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Move out eompi test folder.

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Adèle Harrissart 2014-06-18 10:07:59 +02:00
commit f6d5ac3c6b
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169
eo/test/mpi/t-mpi-multistart.cpp
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@ -1,169 +0,0 @@
# include <mpi/eoMultiStart.h>
using namespace eo::mpi;
#include <stdexcept>
#include <iostream>
#include <sstream>
#include <eo>
#include <es.h>
/*
* This file is based on the tutorial lesson 1. We'll consider that you know all the EO
* related parts of the algorithm and we'll focus our attention on parallelization.
*
* This file shows an example of multistart applied to a eoSGA (simple genetic
* algorithm). As individuals need to be serialized, we implement a class inheriting
* from eoReal (which is the base individual), so as to manipulate individuals as they
* were eoReal AND serialize them.
*
* The main function shows how to launch a multistart job, with default functors. If you
* don't know which functors to use, these ones should fit the most of your purposes.
*/
using namespace std;
/*
* eoReal is a vector of double: we just have to serializes the value and the fitness.
*/
class SerializableEOReal: public eoReal<double>, public eoserial::Persistent
{
public:
SerializableEOReal(unsigned size = 0, double value = 0.0) :
eoReal<double>(size, value)
{
// empty
}
void unpack( const eoserial::Object* obj )
{
this->clear();
eoserial::unpackArray
< std::vector<double>, eoserial::Array::UnpackAlgorithm >
( *obj, "vector", *this );
bool invalidFitness;
eoserial::unpack( *obj, "invalid_fitness", invalidFitness );
if( invalidFitness )
{
this->invalidate();
} else
{
double f;
eoserial::unpack( *obj, "fitness", f );
this->fitness( f );
}
}
eoserial::Object* pack( void ) const
{
eoserial::Object* obj = new eoserial::Object;
obj->add( "vector", eoserial::makeArray< std::vector<double>, eoserial::MakeAlgorithm >( *this ) );
bool invalidFitness = this->invalid();
obj->add( "invalid_fitness", eoserial::make( invalidFitness ) );
if( !invalidFitness )
{
obj->add( "fitness", eoserial::make( this->fitness() ) );
}
return obj;
}
};
// REPRESENTATION
//-----------------------------------------------------------------------------
// define your individuals
typedef SerializableEOReal Indi;
// EVAL
//-----------------------------------------------------------------------------
// a simple fitness function that computes the euclidian norm of a real vector
// @param _indi A real-valued individual
double real_value(const Indi & _indi)
{
double sum = 0;
for (unsigned i = 0; i < _indi.size(); i++)
sum += _indi[i]*_indi[i];
return (-sum); // maximizing only
}
/************************** PARALLELIZATION JOB *******************************/
int main(int argc, char **argv)
{
Node::init( argc, argv );
// PARAMETRES
// all parameters are hard-coded!
const unsigned int SEED = 133742; // seed for random number generator
const unsigned int VEC_SIZE = 8; // Number of object variables in genotypes
const unsigned int POP_SIZE = 100; // Size of population
const unsigned int T_SIZE = 3; // size for tournament selection
const unsigned int MAX_GEN = 100; // Maximum number of generation before STOP
const float CROSS_RATE = 0.8; // Crossover rate
const double EPSILON = 0.01; // range for real uniform mutation
const float MUT_RATE = 0.5; // mutation rate
eoEvalFuncPtr<Indi> eval( real_value );
eoPop<Indi> pop;
eoUniformGenerator< double > generator;
eoInitFixedLength< Indi > init( VEC_SIZE, generator );
pop = eoPop<Indi>( POP_SIZE, init );
eoDetTournamentSelect<Indi> select(T_SIZE);
eoSegmentCrossover<Indi> xover;
eoUniformMutation<Indi> mutation(EPSILON);
eoGenContinue<Indi> continuator(MAX_GEN);
/* Does work too with a steady fit continuator. */
// eoSteadyFitContinue< Indi > continuator( 10, 50 );
eoSGA<Indi> gga(select, xover, CROSS_RATE, mutation, MUT_RATE,
eval, continuator);
/* How to assign tasks, which are starts? */
DynamicAssignmentAlgorithm assignmentAlgo;
/* Before a worker starts its algorithm, how does it reinits the population?
* There are a few default usable functors, defined in eoMultiStart.h.
*
* This one (ReuseSamePopEA) doesn't modify the population after a start, so
* the same population is reevaluated on each multistart: the solution tend
* to get better and better.
*/
ReuseSamePopEA< Indi > resetAlgo( continuator, pop, eval );
/**
* How to send seeds to the workers, at the beginning of the parallel job?
* This functors indicates that seeds should be random values.
*/
GetRandomSeeds< Indi > getSeeds( SEED );
// Builds the store
MultiStartStore< Indi > store(
gga,
DEFAULT_MASTER,
resetAlgo,
getSeeds);
// Creates the multistart job and runs it.
// The last argument indicates that we want to launch 5 runs.
MultiStart< Indi > msjob( assignmentAlgo, DEFAULT_MASTER, store, 5 );
msjob.run();
if( msjob.isMaster() )
{
msjob.best_individuals().sort();
std::cout << "Global best individual has fitness " << msjob.best_individuals().best_element().fitness() << std::endl;
}
MultiStart< Indi > msjob10( assignmentAlgo, DEFAULT_MASTER, store, 10 );
msjob10.run();
if( msjob10.isMaster() )
{
msjob10.best_individuals().sort();
std::cout << "Global best individual has fitness " << msjob10.best_individuals().best_element().fitness() << std::endl;
}
return 0;
}