本文整理汇总了C++中Population::AccessInnovationDatabase方法的典型用法代码示例。如果您正苦于以下问题：C++ Population::AccessInnovationDatabase方法的具体用法？C++ Population::AccessInnovationDatabase怎么用？C++ Population::AccessInnovationDatabase使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Population的用法示例。
在下文中一共展示了Population::AccessInnovationDatabase方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: MutateGenome
// Mutates a genome
void Species::MutateGenome( bool t_baby_is_clone, Population &a_Pop, Genome &t_baby, Parameters& a_Parameters, RNG& a_RNG )
{
#if 1
// NEW version:
// All mutations are mutually exclusive - can't have 2 mutations at once
// for example a weight mutation and time constants mutation
// or add link and add node and then weight mutation
// We will perform roulette wheel selection to choose the type of mutation and will mutate the baby
// This method guarantees that the baby will be mutated at least with one mutation
enum MutationTypes {ADD_NODE = 0, ADD_LINK, REMOVE_NODE, REMOVE_LINK, CHANGE_ACTIVATION_FUNCTION,
MUTATE_WEIGHTS, MUTATE_ACTIVATION_A, MUTATE_ACTIVATION_B, MUTATE_TIMECONSTS, MUTATE_BIASES
};
std::vector<int> t_muts;
std::vector<double> t_mut_probs;
// ADD_NODE;
t_mut_probs.push_back( a_Parameters.MutateAddNeuronProb );
// ADD_LINK;
t_mut_probs.push_back( a_Parameters.MutateAddLinkProb );
// REMOVE_NODE;
t_mut_probs.push_back( a_Parameters.MutateRemSimpleNeuronProb );
// REMOVE_LINK;
t_mut_probs.push_back( a_Parameters.MutateRemLinkProb );
// CHANGE_ACTIVATION_FUNCTION;
t_mut_probs.push_back( a_Parameters.MutateNeuronActivationTypeProb );
// MUTATE_WEIGHTS;
t_mut_probs.push_back( a_Parameters.MutateWeightsProb );
// MUTATE_ACTIVATION_A;
t_mut_probs.push_back( a_Parameters.MutateActivationAProb );
// MUTATE_ACTIVATION_B;
t_mut_probs.push_back( a_Parameters.MutateActivationBProb );
// MUTATE_TIMECONSTS;
t_mut_probs.push_back( a_Parameters.MutateNeuronTimeConstantsProb );
// MUTATE_BIASES;
t_mut_probs.push_back( a_Parameters.MutateNeuronBiasesProb );
// Special consideration for phased searching - do not allow certain mutations depending on the search mode
// also don't use additive mutations if we just want to get rid of the clones
if ((a_Pop.GetSearchMode() == SIMPLIFYING) || t_baby_is_clone)
{
t_mut_probs[ADD_NODE] = 0; // add node
t_mut_probs[ADD_LINK] = 0; // add link
}
if ((a_Pop.GetSearchMode() == COMPLEXIFYING) || t_baby_is_clone)
{
t_mut_probs[REMOVE_NODE] = 0; // rem node
t_mut_probs[REMOVE_LINK] = 0; // rem link
}
bool t_mutation_success = false;
// repeat until successful
while (t_mutation_success == false)
{
int ChosenMutation = a_RNG.Roulette(t_mut_probs);
// Now mutate based on the choice
switch(ChosenMutation)
{
case ADD_NODE:
t_mutation_success = t_baby.Mutate_AddNeuron(a_Pop.AccessInnovationDatabase(), a_Parameters, a_RNG);
break;
case ADD_LINK:
t_mutation_success = t_baby.Mutate_AddLink(a_Pop.AccessInnovationDatabase(), a_Parameters, a_RNG);
break;
case REMOVE_NODE:
t_mutation_success = t_baby.Mutate_RemoveSimpleNeuron(a_Pop.AccessInnovationDatabase(), a_RNG);
break;
case REMOVE_LINK:
{
// Keep doing this mutation until it is sure that the baby will not
// end up having dead ends or no links
Genome t_saved_baby = t_baby;
bool t_no_links = false, t_has_dead_ends = false;
int t_tries = 128;
do
{
t_tries--;
if (t_tries <= 0)
{
t_saved_baby = t_baby;
break; // give up
}
t_saved_baby = t_baby;
t_mutation_success = t_saved_baby.Mutate_RemoveLink(a_RNG);
//.........这里部分代码省略.........