Algoritmi genetici pentru rezolvarea problemelor prin - Sorin ...

Population.cpp
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}
};
k += delta;
assert (_population_size _cross_over_rate;
const int mutation_factor = int(100 * _ga->_mutation_rate);
assert (0 < mutation_factor);
const int cross_overs = int((_population_size * cross_over_rate) / 2);
for(int i = 0; i < cross_overs; i++)
{
const int i1 = rand() % _population_size;
const int i2 = rand() % _population_size;
};
_next_generation->_genomes.at(i1).Crossover(_next_generation->_genomes.at(i2));
for (i = 0; i < _population_size; i++)
{
if ((rand() % 100) < mutation_factor)
{
_next_generation->_genomes.at(i).Mutate();
};
};
void Population::post_step()
{
}
for (int i = 0; i < _genomes.size(); i++)
{
_genomes.at(i) = _next_generation->_genomes.at(i);
_genomes.at(i)._gaFitness = -1;
_genomes.at(i)._populationFitness = -1;
};
_best_genome = NULL;
// clean up
assert (NULL != _next_generation);
delete _next_generation;
_next_generation = NULL;
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