// initializes a species with a leader genome and an ID number
Species::Species(const Genome& a_Genome, int a_ID)
{
m_ID = a_ID;
// copy the initializing genome locally.
// it is now the representative of the species.
m_Representative = a_Genome;
m_BestGenome = a_Genome;
// add the first and only one individual
m_Individuals.push_back(a_Genome);
m_Age = 0;
m_GensNoImprovement = 0;
m_OffspringRqd = 0;
m_BestFitness = a_Genome.GetFitness();
m_BestSpecies = true;
// Choose a random color
RNG rng;
rng.TimeSeed();
m_R = static_cast<int>(rng.RandFloat() * 255);
m_G = static_cast<int>(rng.RandFloat() * 255);
m_B = static_cast<int>(rng.RandFloat() * 255);
}
// returns an individual randomly selected from the best N%
Genome Species::GetIndividual(Parameters& a_Parameters, RNG& a_RNG) const
{
ASSERT(m_Individuals.size() > 0);
// Make a pool of only evaluated individuals!
std::vector<Genome> t_Evaluated;
for(unsigned int i=0; i<m_Individuals.size(); i++)
{
if (m_Individuals[i].IsEvaluated())
t_Evaluated.push_back( m_Individuals[i] );
}
ASSERT(t_Evaluated.size() > 0);
if (t_Evaluated.size() == 1)
{
return (t_Evaluated[0]);
}
else if (t_Evaluated.size() == 2)
{
return (t_Evaluated[ Rounded(a_RNG.RandFloat()) ]);
}
// Warning!!!! The individuals must be sorted by best fitness for this to work
int t_chosen_one=0;
// Here might be introduced better selection scheme, but this works OK for now
if (!a_Parameters.RouletteWheelSelection)
{ //start with the last one just for comparison sake
int temp_genome;
int t_num_parents = static_cast<int>( floor((a_Parameters.SurvivalRate * (static_cast<double>(t_Evaluated.size())))+1.0));
ASSERT(t_num_parents>0);
if (t_num_parents>=t_Evaluated.size())
t_num_parents = t_Evaluated.size()-1;
t_chosen_one = a_RNG.RandInt(0, t_num_parents);
for (unsigned int i = 0; i < a_Parameters.TournamentSize; i++)
{
temp_genome = a_RNG.RandInt(0, t_num_parents);
if (m_Individuals[temp_genome].GetFitness() > m_Individuals[t_chosen_one].GetFitness())
{
t_chosen_one = temp_genome;
}
}
}
else
{
// roulette wheel selection
std::vector<double> t_probs;
for(unsigned int i=0; i<t_Evaluated.size(); i++)
t_probs.push_back( t_Evaluated[i].GetFitness() );
t_chosen_one = a_RNG.Roulette(t_probs);
}
return (t_Evaluated[t_chosen_one]);
}
// 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);
t_no_links = t_has_dead_ends = false;
if (t_saved_baby.NumLinks() == 0)
t_no_links = true;
t_has_dead_ends = t_saved_baby.HasDeadEnds();
}
while(t_no_links || t_has_dead_ends);
t_baby = t_saved_baby;
// debugger trap
if (t_baby.NumLinks() == 0)
{
std::cerr << "No links in baby after mutation" << std::endl;
}
if (t_baby.HasDeadEnds())
{
std::cerr << "Dead ends in baby after mutation" << std::endl;
}
}
break;
case CHANGE_ACTIVATION_FUNCTION:
t_baby.Mutate_NeuronActivation_Type(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_WEIGHTS:
t_baby.Mutate_LinkWeights(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_ACTIVATION_A:
t_baby.Mutate_NeuronActivations_A(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_ACTIVATION_B:
t_baby.Mutate_NeuronActivations_B(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_TIMECONSTS:
t_baby.Mutate_NeuronTimeConstants(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_BIASES:
t_baby.Mutate_NeuronBiases(a_Parameters, a_RNG);
t_mutation_success = true;
break;
default:
t_mutation_success = false;
break;
}
}
#else
// Old version of the function - added just to test various ways to do mutation
bool t_mutation_success = false;
// repeat until successful
while (t_mutation_success == false)
{
if (a_RNG.RandFloat() < a_Parameters.MutateAddNeuronProb)
t_mutation_success = t_baby.Mutate_AddNeuron(a_Pop.AccessInnovationDatabase(), a_Parameters, a_RNG);
else
if (a_RNG.RandFloat() < a_Parameters.MutateAddLinkProb)
t_mutation_success = t_baby.Mutate_AddLink(a_Pop.AccessInnovationDatabase(), a_Parameters, a_RNG);
else
{
/*if (a_RNG.RandFloat() < a_Parameters.MutateNeuronActivationTypeProb)
{
t_baby.Mutate_NeuronActivation_Type(a_Parameters, a_RNG);
t_mutation_success = true;
}*/
if (a_RNG.RandFloat() < a_Parameters.MutateWeightsProb)
{
t_baby.Mutate_LinkWeights(a_Parameters, a_RNG);
t_mutation_success = true;
break;
}
/*case MUTATE_ACTIVATION_A:
t_baby.Mutate_NeuronActivations_A(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_ACTIVATION_B:
t_baby.Mutate_NeuronActivations_B(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_TIMECONSTS:
t_baby.Mutate_NeuronTimeConstants(a_Parameters, a_RNG);
t_mutation_success = true;
break;
case MUTATE_BIASES:
t_baby.Mutate_NeuronBiases(a_Parameters, a_RNG);
t_mutation_success = true;
break;*/
}
}
#endif
}
Genome Species::ReproduceOne(Population& a_Pop, Parameters& a_Parameters, RNG& a_RNG)
{
Genome t_baby; // for storing the result
//////////////////////////
// Reproduction
// Spawn only one baby
// this tells us if the baby is a result of mating
bool t_mated = false;
// There must be individuals there..
ASSERT(NumIndividuals() > 0);
// for a species of size 1 we can only mutate
// NOTE: but does it make sense since we know this is the champ?
if (NumIndividuals() == 1)
{
t_baby = GetIndividual(a_Parameters, a_RNG);
t_mated = false;
}
// else we can mate
else
{
Genome t_mom = GetIndividual(a_Parameters, a_RNG);
// choose whether to mate at all
// Do not allow crossover when in simplifying phase
if ((a_RNG.RandFloat() < a_Parameters.CrossoverRate) && (a_Pop.GetSearchMode() != SIMPLIFYING))
{
// get the father
Genome t_dad;
bool t_interspecies = false;
// There is a probability that the father may come from another species
if ((a_RNG.RandFloat() < a_Parameters.InterspeciesCrossoverRate) && (a_Pop.m_Species.size()>1))
{
// Find different species (random one) // !!!!!!!!!!!!!!!!!
// But the different species must have at least one evaluated individual
int t_diffspec = 0;
int t_giveup = 64;
do
{
t_diffspec = a_RNG.RandInt(0, static_cast<int>(a_Pop.m_Species.size()-1));
}
while ((a_Pop.m_Species[t_diffspec].m_AverageFitness == 0) && (t_giveup--));
if (a_Pop.m_Species[t_diffspec].m_AverageFitness == 0)
t_dad = GetIndividual(a_Parameters, a_RNG);
else
t_dad = a_Pop.m_Species[t_diffspec].GetIndividual(a_Parameters, a_RNG);
t_interspecies = true;
}
else
{
// Mate within species
t_dad = GetIndividual(a_Parameters, a_RNG);
// The other parent should be a different one
// number of tries to find different parent
int t_tries = 32;
while(((t_mom.GetID() == t_dad.GetID()) || ((!a_Parameters.AllowClones) && (t_mom.CompatibilityDistance(t_dad, a_Parameters) <= 0.00001)) ) && (t_tries--))
{
t_dad = GetIndividual(a_Parameters, a_RNG);
}
t_interspecies = false;
}
// OK we have both mom and dad so mate them
// Choose randomly one of two types of crossover
if (a_RNG.RandFloat() < a_Parameters.MultipointCrossoverRate)
{
t_baby = t_mom.Mate( t_dad, false, t_interspecies, a_RNG);
}
else
{
t_baby = t_mom.Mate( t_dad, true, t_interspecies, a_RNG);
}
t_mated = true;
}
// don't mate - reproduce the mother asexually
else
{
t_baby = t_mom;
t_mated = false;
}
}
/* if (t_baby.HasDeadEnds())
{
std::cout << "Dead ends in baby after crossover" << std::endl;
// int p;
// std::cin >> p;
}*/
// OK we have the baby, so let's mutate it.
bool t_baby_is_clone = false;
if ((!t_mated) || (a_RNG.RandFloat() < a_Parameters.OverallMutationRate))
MutateGenome(t_baby_is_clone, a_Pop, t_baby, a_Parameters, a_RNG);
// We have a new offspring now
// give the offspring a new ID
t_baby.SetID(a_Pop.GetNextGenomeID());
a_Pop.IncrementNextGenomeID();
// sort the baby's genes
t_baby.SortGenes();
// clear the baby's fitness
t_baby.SetFitness(0);
t_baby.SetAdjFitness(0);
t_baby.SetOffspringAmount(0);
t_baby.ResetEvaluated();
// debug trap
/* if (t_baby.NumLinks() == 0)
{
std::cout << "No links in baby after reproduction" << std::endl;
// int p;
// std::cin >> p;
}
if (t_baby.HasDeadEnds())
{
std::cout << "Dead ends in baby after reproduction" << std::endl;
// int p;
// std::cin >> p;
}
*/
return t_baby;
}
// Reproduce mates & mutates the individuals of the species
// It may access the global species list in the population
// because some babies may turn out to belong in another species
// that have to be created.
// Also calls Birth() for every new baby
void Species::Reproduce(Population &a_Pop, Parameters& a_Parameters, RNG& a_RNG)
{
Genome t_baby; // temp genome for reproduction
int t_offspring_count = Rounded(GetOffspringRqd());
// no offspring?! yikes.. dead species!
if (t_offspring_count == 0)
{
// maybe do something else?
return;
}
//////////////////////////
// Reproduction
// Spawn t_offspring_count babies
bool t_champ_chosen = false;
bool t_baby_exists_in_pop = false;
while(t_offspring_count--)
{
bool t_new_individual = true;
// if the champ was not chosen, do it now..
if (!t_champ_chosen)
{
t_baby = m_Individuals[0];
t_champ_chosen = true;
t_new_individual = false;
}
// or if it was, then proceed with the others
else
{
do // - while the baby already exists somewhere in the new population
{
// this tells us if the baby is a result of mating
bool t_mated = false;
// There must be individuals there..
ASSERT(NumIndividuals() > 0);
// for a species of size 1 we can only mutate
// NOTE: but does it make sense since we know this is the champ?
if (NumIndividuals() == 1)
{
t_baby = GetIndividual(a_Parameters, a_RNG);
t_mated = false;
}
// else we can mate
else
{
do // keep trying to mate until a good offspring is produced
{
Genome t_mom = GetIndividual(a_Parameters, a_RNG);
// choose whether to mate at all
// Do not allow crossover when in simplifying phase
if ((a_RNG.RandFloat() < a_Parameters.CrossoverRate) && (a_Pop.GetSearchMode() != SIMPLIFYING))
{
// get the father
Genome t_dad;
bool t_interspecies = false;
// There is a probability that the father may come from another species
if ((a_RNG.RandFloat() < a_Parameters.InterspeciesCrossoverRate) && (a_Pop.m_Species.size()>1))
{
// Find different species (random one) // !!!!!!!!!!!!!!!!!
int t_diffspec = a_RNG.RandInt(0, static_cast<int>(a_Pop.m_Species.size()-1));
t_dad = a_Pop.m_Species[t_diffspec].GetIndividual(a_Parameters, a_RNG);
t_interspecies = true;
}
else
{
// Mate within species
t_dad = GetIndividual(a_Parameters, a_RNG);
// The other parent should be a different one
// number of tries to find different parent
int t_tries = 32;
if (!a_Parameters.AllowClones)
while(((t_mom.GetID() == t_dad.GetID()) || (t_mom.CompatibilityDistance(t_dad, a_Parameters) < 0.00001) ) && (t_tries--))
{
t_dad = GetIndividual(a_Parameters, a_RNG);
}
else
while(((t_mom.GetID() == t_dad.GetID()) ) && (t_tries--))
{
t_dad = GetIndividual(a_Parameters, a_RNG);
}
t_interspecies = false;
}
// OK we have both mom and dad so mate them
// Choose randomly one of two types of crossover
if (a_RNG.RandFloat() < a_Parameters.MultipointCrossoverRate)
{
t_baby = t_mom.Mate( t_dad, false, t_interspecies, a_RNG);
}
else
{
t_baby = t_mom.Mate( t_dad, true, t_interspecies, a_RNG);
}
t_mated = true;
}
// don't mate - reproduce the mother asexually
else
{
t_baby = t_mom;
t_mated = false;
}
} while (t_baby.HasDeadEnds() || (t_baby.NumLinks() == 0));
// in case of dead ends after crossover we will repeat crossover
// until it works
}
// Mutate the baby
if ((!t_mated) || (a_RNG.RandFloat() < a_Parameters.OverallMutationRate))
MutateGenome(t_baby_exists_in_pop, a_Pop, t_baby, a_Parameters, a_RNG);
// Check if this baby is already present somewhere in the offspring
// we don't want that
t_baby_exists_in_pop = false;
// Unless of course, we want
if (!a_Parameters.AllowClones)
{
for(unsigned int i=0; i<a_Pop.m_TempSpecies.size(); i++)
{
for(unsigned int j=0; j<a_Pop.m_TempSpecies[i].m_Individuals.size(); j++)
{
if (
(t_baby.CompatibilityDistance(a_Pop.m_TempSpecies[i].m_Individuals[j], a_Parameters) < 0.00001) // identical genome?
)
{
t_baby_exists_in_pop = true;
break;
}
}
}
}
}
while (t_baby_exists_in_pop); // end do
}
// Final place to test for problems
// If there is anything wrong here, we will just
// pick a random individual and leave him unchanged
if ((t_baby.NumLinks() == 0) || t_baby.HasDeadEnds())
{
t_baby = GetIndividual(a_Parameters, a_RNG);
t_new_individual = false;
}
if (t_new_individual) {
// We have a new offspring now
// give the offspring a new ID
t_baby.SetID(a_Pop.GetNextGenomeID());
a_Pop.IncrementNextGenomeID();
// sort the baby's genes
t_baby.SortGenes();
// clear the baby's fitness
t_baby.SetFitness(0);
t_baby.SetAdjFitness(0);
t_baby.SetOffspringAmount(0);
t_baby.ResetEvaluated();
}
//////////////////////////////////
// put the baby to its species //
//////////////////////////////////
// before Reproduce() is invoked, it is assumed that a
// clone of the population exists with the name of m_TempSpecies
// we will store results there.
// after all reproduction completes, the original species will be replaced back
bool t_found = false;
std::vector<Species>::iterator t_cur_species = a_Pop.m_TempSpecies.begin();
// No species yet?
if (t_cur_species == a_Pop.m_TempSpecies.end())
{
// create the first species and place the baby there
a_Pop.m_TempSpecies.push_back( Species(t_baby, a_Pop.GetNextSpeciesID()));
a_Pop.IncrementNextSpeciesID();
}
else
{
// try to find a compatible species
Genome t_to_compare = t_cur_species->GetRepresentative();
t_found = false;
while((t_cur_species != a_Pop.m_TempSpecies.end()) && (!t_found))
{
if (t_baby.IsCompatibleWith( t_to_compare, a_Parameters))
{
// found a compatible species
t_cur_species->AddIndividual(t_baby);
t_found = true; // the search is over
}
else
{
// keep searching for a matching species
t_cur_species++;
if (t_cur_species != a_Pop.m_TempSpecies.end())
{
t_to_compare = t_cur_species->GetRepresentative();
}
}
}
// if couldn't find a match, make a new species
if (!t_found)
{
a_Pop.m_TempSpecies.push_back( Species(t_baby, a_Pop.GetNextSpeciesID()));
a_Pop.IncrementNextSpeciesID();
}
}
}
}
