void MimicGa::_updateScores()
{
GeneticAlgorithm::_updateScores();
// sort in descending order
sort(_population.begin(), _population.end(), CompareGenomes());
WeightMap oldWeights = _weights;
_weights.clear();
// @todo test this
// // if we're stagnating, start bringing the weights back together. This should help broaden
// // the search a bit.
// if (_lastBest == _population[0]->getScore())
// {
// _stagnation++;
// for (WeightMap::iterator it = oldWeights.begin(); it != oldWeights.end(); ++it)
// {
// it->second = pow(it->second, 1.0 / (double)_stagnation);
// }
// _normalize(oldWeights);
// }
boost::shared_ptr<CalculatorGenome> cg = boost::dynamic_pointer_cast<CalculatorGenome>(_population[0]);
const CalculatorGenome::AvailableNodeMap& anm = cg->getAvailableNodes();
for (CalculatorGenome::AvailableNodeMap::const_iterator it = anm.begin(); it != anm.end();
++it)
{
_weights[it->first] = 1e-5;
}
for (unsigned int i = 0; i < _population.size() * _thetaPercentile; i++)
{
cg = boost::dynamic_pointer_cast<CalculatorGenome>(_population[i]);
assert(cg);
_populateWeights(cg->getRoot());
}
// normalize the weights so they sum to 1
_normalize(_weights);
if (oldWeights.size() > 0)
{
for (WeightMap::iterator it = _weights.begin(); it != _weights.end(); ++it)
{
it->second = it->second * _learningRate + oldWeights[it->first] * (1 - _learningRate);
//cout << it->first << "\t" << it->second << endl;
}
}
// for (WeightMap::iterator it = _weights.begin(); it != _weights.end(); ++it)
// {
// cout << it->first << "\t" << it->second << endl;
// }
}
void GeneticAlgorithm::step()
{
// the first step is just initialization
if (_initialized == false)
{
initialize();
sort(_population.begin(), _population.end(), CompareGenomes());
return;
}
if (_best->getScore() == numeric_limits<double>::infinity())
{
return;
}
// sort in descending order
sort(_population.begin(), _population.end(), CompareGenomes());
vector< shared_ptr<Genome> > nextGen;
nextGen.reserve(_populationSize);
for (int i = 0; i < _keepBest && i < (int)_population.size(); i++)
{
nextGen.push_back(_population[i]->clone());
}
for (int i = 0; i < _keepBest && i < (int)_population.size(); i++)
{
shared_ptr<Genome> g = _population[i]->clone();
g->mutate(_mutationSeverity);
nextGen.push_back(g);
}
for (int i = 0; i < _freshMeat; i++)
{
shared_ptr<Genome> g = _seed->clone();
_initializeGenome(g);
if (_used.find(g->toString()) == _used.end())
{
nextGen.push_back(g);
_used.insert(g->toString());
}
}
while ((int)nextGen.size() < _populationSize)
{
// if (nextGen.size() % 500 == 0 && nextGen.size() > 0)
// {
// cout << nextGen.size() << "/" << _populationSize << "\r";
// cout.flush();
// }
// choose the act of 'god'
double act = Tgs::Random::instance()->generateUniform();
//double mutate = Tgs::Random::instance()->generateUniform();
// if they get a free pass, randomly select a genome and pass it on to the next gen
if (act < _mutationProb)
{
int index = _selectMateRoulette();
shared_ptr<Genome> genome = _population[index]->clone();
_mutate(genome);
// // make sure at least half of the new generation is unique
// if ((int)nextGen.size() < _populationSize / 4)
// {
// if (_used.find(genome->toString()) == _used.end())
// {
// nextGen.push_back(genome);
// }
// }
// else
{
nextGen.push_back(genome);
}
}
// otherwise we're mating something
else
{
int fatherIndex = _selectMateTournament();
int motherIndex = _selectMateTournament();
if (fatherIndex != motherIndex)
{
shared_ptr<Genome> father = _population[fatherIndex];
shared_ptr<Genome> mother = _population[motherIndex];
shared_ptr<Genome> brother, sister;
father->crossoverSexually(*father, *mother, brother, sister);
// if (mutate < _mutationProb)
// {
// brother->mutate(_mutationSeverity);
// sister->mutate(_mutationSeverity);
// }
// // make sure at least one fourth of the new generation is unique
// if ((int)nextGen.size() < _populationSize / 4)
// {
// if (_used.find(brother->toString()) == _used.end())
// {
// nextGen.push_back(brother);
// _used.insert(brother->toString());
// }
// if ((int)nextGen.size() < _populationSize)
// {
// if (_used.find(sister->toString()) == _used.end())
// {
// nextGen.push_back(sister);
// _used.insert(sister->toString());
// }
// }
// }
// else
{
nextGen.push_back(brother);
_used.insert(brother->toString());
nextGen.push_back(sister);
_used.insert(sister->toString());
}
}
}
}
//cout << " \r";
_population = nextGen;
_updateScores();
// // sort in descending order
// sort(_population.begin(), _population.end(), CompareGenomes());
// nextGen.clear();
// string lastStr = _population[0]->toString();
// nextGen.push_back(_population[0]);
// for (unsigned int i = 1; i < _population.size(); i++)
// {
// if (_population[i]->toString() != lastStr)
// {
// nextGen.push_back(_population[i]);
// lastStr = _population[i]->toString();
// }
// }
// _population = nextGen;
}
