C++ Genome::clone方法代码示例

int main() {
    const unsigned mutations=2;
    Genome::set_mutations_at_cloning(mutations);
    std::cout<<"[Cloning with "<<mutations<<" mutations.]"<<std::endl;
    
    Genome A;
    std::cout<<"HEALTHY GENOME: "<<std::endl<<" "<<A<<std::endl;
    std::cout<<" Bad mutations within first 10 years = "<<A.bad_mutations(10)<<std::endl;
    Genome B=A.clone();
    std::cout<<"AFTER 1st CLONING: "<<std::endl<<" "<<B<<std::endl;
    std::cout<<" Bad mutations within the whole Genome = "<<B.bad_mutations(Genome::max_age-1)<<std::endl;
    for (unsigned i=0; i<5; ++i) {
        A=B.clone();
        B=A.clone();
    }
    std::cout<<"AFTER 10 MORE CLONINGS: "<<std::endl<<" "<<A<<std::endl;
    std::cout<<" Bad mutations within first 5 years = "<<A.bad_mutations(5)<<std::endl;
    std::cout<<"AFTER 1 MORE CLONING: "<<std::endl<<" "<<B<<std::endl;
    std::cout<<" Bad mutations within first 10 years = "<<A.bad_mutations(10)<<std::endl;
    for (unsigned i=0; i<50; ++i) {
        A=B.clone();
        B=A.clone();
    }
    std::cout<<"AFTER 100 MORE CLONINGS: "<<std::endl<<" "<<A<<std::endl;
    std::cout<<" Bad mutations within first 2 years = "<<A.bad_mutations(2)<<std::endl;
    std::cout<<"AFTER 1 MORE CLONING: "<<std::endl<<" "<<B<<std::endl;
    std::cout<<" Bad mutations within first 2 years = "<<A.bad_mutations(2)<<std::endl;
    
    return 0;
}

int main()
{

    Genome::set_mutations_at_cloning(Genome::max_age-Genome::max_age/4);
    Genome gene;
    Genome gene2=gene.clone();

    cout<<gene;
    cout<<gene2;


}

  void CalculatorGenome::crossoverSexually(const Genome& father, const Genome& mother, 
    shared_ptr<Genome>& brother, shared_ptr<Genome>& sister)
  {
    shared_ptr<CalculatorGenome> bro = dynamic_pointer_cast<CalculatorGenome>(father.clone());
    shared_ptr<CalculatorGenome> sis = dynamic_pointer_cast<CalculatorGenome>(mother.clone());

    // find the cut points
    shared_ptr<CalculatorGenomeNode> broSubtree = bro->_chooseRandomNode();
    shared_ptr<CalculatorGenomeNode> sisSubtree = sis->_chooseRandomNode();

    shared_ptr<CalculatorGenomeNode> broSubtreeParent = bro->_findParent(broSubtree);
    shared_ptr<CalculatorGenomeNode> sisSubtreeParent = sis->_findParent(sisSubtree);

    // move the sister subtree over to the bro
    if (broSubtreeParent == NULL)
    {
      bro->_root = sisSubtree;
    }
    else
    {
      std::string broLabel = broSubtreeParent->findInput(broSubtree);
      broSubtreeParent->setInput(broLabel, sisSubtree);
    }

    // move the brother subtree over to the bro
    if (sisSubtreeParent == NULL)
    {
      sis->_root = broSubtree;
    }
    else
    {
      std::string sisLabel = sisSubtreeParent->findInput(sisSubtree);
      sisSubtreeParent->setInput(sisLabel, broSubtree);
    }

    brother = bro;
    brother->setScore(-1);
    sister = sis;
    sister->setScore(-1);
  }

void SteadyStateGA::nextGeneration()
{
	double cross_random;
	double mutate_random;

	// sort the population bases on the parameters
	pop->sortPopulation();

	vector<Genome *> *new_genomes = new vector<Genome *>;

	// we only replace replace_percentage of the population.
	// decide which percentage to replace...the lowest scores... (-1) fitnesses
	// reverse interate through and delete the genomes.

	// copy the top (pop_size * replacement_percentage) genomes into the new genome vector
	for(int i = 0; i < (int)(pop->getPopSize() * replace_percentage); ++i)
	{
		new_genomes->push_back(pop->getGenome(i)->clone());
	}


	// now i need to add 1 - pop->getPopSize() * replace_percentage more genomes
	for(int i = 0; i < (int)(pop->getPopSize() - (pop->getPopSize() * replace_percentage)); ++i)
	{
		// need to select a dad and mom .. select...
		Genome *dad = pop->select();
		Genome *mom = pop->select();	
	

		// then create child for crossover.
		Genome *child;

		cross_random = objRand->randomPercentage();
		// if crossover does not happen make the children = to the parents
		if(cross_random <= crossover_percentage)
		{
			// perform crossover
			child = dad->crossover(*mom);
		}
		else
		{
			// make child and dad equal to each other
			child = dad->clone();

		}

		mutate_random = objRand->randomPercentage();
		// chance of mutation to each of the children
		if(mutate_random <= mutation_percentage)
		{
			// perform crossover
			child->mutate();
		}

		if((int)new_genomes->size() < pop->getPopSize())
			new_genomes->push_back(child);

	}

	
	++current_generation;

	pop->setPopGenomes(new_genomes);
	init(); // init the new population
}