double operator()(Individual& ind, RNG& rng, EA& ea) {
// get the phenotype (markov network):
typename EA::phenotype_type &N = ealib::phenotype(ind, ea);
// probably want to reset the RNG for the markov network:
N.reset(rng.seed());
// now, set the values of the bits in the input vector:
double f=0.0;
for(std::size_t i=0; i<128; ++i) {
// allocate space for the inputs:
std::vector<int> inputs;//(net.ninput_states(), 0);
inputs.push_back(rng.bit());
inputs.push_back(rng.bit());
// update the network n times:
N.clear();
N.update(inputs);
if(*N.begin_output() == (inputs[0] ^ inputs[1])) {
++f;
}
}
// and return some measure of fitness:
return f;
}
Status GetRandomMatrix(int n, int ix, SpdMatrixT& orig, SpdMatrixT& reduced)
{
typedef std::mt19937 RNG; // the Mersenne Twister with a popular choice of parameters
// http://stackoverflow.com/questions/7114043/random-number-generation-in-c11-how-to-generate-how-do-they-work
std::uniform_int_distribution<int> dist(-5, 5);
Matrix2D tmp(n);
RNG rng;
rng.seed();
for( int i = 0; i < n; ++i )
{
tmp[i].resize(2*n);
for( int j = 0; j < 2*n; ++j )
{
tmp[i][j] = dist(rng);
}
}
Size_T ij = 0;
for( int i = 0; i < n; ++i )
{
for( int j = 0; j <= i; ++j )
{
double s = 0.0;
for( int k = 0; k < 2*n; ++k )
{
s += tmp[i][k] * tmp[j][k];
}
orig[ij++] = s;
}
}
Compress(n, ix, orig, reduced);
return Status::Success;
}
double operator()(Individual& ind, RNG& rng, EA& ea) {
typedef sequence_matrix<data::db_type::record_type::vector_type> matrix_type;
typedef retina2_iterator<matrix_type> iterator_type;
// lazy load of the mnist data (so we don't have to wait unless we
// absolutely have to).
data::instance()->initialize(get<EVOCADX_TRAIN_FILE>(ea), get<EVOCADX_TEST_FILE>(ea));
// get a markov network:
typename EA::phenotype_type &N = ealib::phenotype(ind, ea);
int seed = rng.seed(); // save the seed
// don't let empty networks play:
if(N.ngates() == 0) {
return 0.0;
}
double w=0.0; // accumulated fitness
// analyze the lidx records:
for(int i=0; i<get<EVOCADX_EXAMINE_N>(ea); ++i) {
N.reset(seed);
N.clear();
// build a matrix facade for the lix record we're looking at:
data::db_type::record_type& R=data::instance()->training[i];
matrix_type M(R.data,
data::instance()->training.dim(0),
data::instance()->training.dim(1));
// now build a retina iterator over this matrix:
iterator_type ci(M, get<EVOCADX_FOVEA_SIZE>(ea), get<EVOCADX_RETINA_SIZE>(ea));
ci.position(M.size1()/2, M.size2()/2);
int updates = get<mkv::MKV_UPDATE_N>(ea);
for(int j=0; j<updates; ++j) {
N.update(ci);
ci.move(algorithm::bits2ternary(N.begin_output()), algorithm::bits2ternary(N.begin_output()+2));
}
std::vector<int> D;
algorithm::range_pair2indices(N.begin_output()+4, N.end_output(), std::back_inserter(D));
if((D.size() == 1) && (D[0] == R.label)) {
w += 1.0;
}
}
return w;
}
int main( int argc, char** argv )
{
RNG rng;
std::vector<uint32_t> seeds;
seeds.push_back( 0x01234567 );
seeds.push_back( 0xFACEFEED );
seeds.push_back( 0x666DEAD0 );
seeds.push_back( 0xDEADC0DE );
TSP<130> ch130;
ch130.LoadFromFile( "data/ch130.tsp" );
printf("===GREEDY===\n");
ch130.Greedy().Debug();
printf("************\n");
TSP<279> a279;
a279.LoadFromFile( "data/a280.tsp" );
printf("===GREEDY===\n");
a279.Greedy().Debug();
printf("************\n");
TSP<654> p654;
p654.LoadFromFile( "data/p654.tsp" );
printf("===GREEDY===\n");
p654.Greedy().Debug();
printf("************\n");
rng.seed( 0xFACEFEED );
MH::Hormigas::SH<TSP<130>> TSP_sh130(
ch130,
rng,
5, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SH<TSP<279>> TSP_sh279(
a279,
rng,
10, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SH<TSP<654>> TSP_sh654(
p654,
rng,
15, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SHE<TSP<130>> TSP_she130(
ch130,
rng,
5, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SHE<TSP<279>> TSP_she279(
a279,
rng,
10, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SHE<TSP<654>> TSP_she654(
p654,
rng,
15, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1 // factor de evaporación
);
MH::Hormigas::SCH<TSP<130>> TSP_sch130(
ch130,
rng,
5, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1f, // factor de evaporación
0.1f,
0.98f
);
MH::Hormigas::SCH<TSP<279>> TSP_sch279(
a279,
rng,
10, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1f, // factor de evaporación
0.1f,
0.98f
);
MH::Hormigas::SCH<TSP<654>> TSP_sch654(
p654,
rng,
15, // minutos de funcionamiento
20, // numero de hormigas
1, // exponente de feromonas
2, // exponente de heuristica
0.1f, // factor de evaporación
0.1f,
0.98f
);
printf("HORMIGAS BASICO\n==================\n");
test( ch130, TSP_sh130, 5, true );
test( a279, TSP_sh279, 5, true );
test( p654, TSP_sh654, 5, true );
printf("HORMIGAS ELITE\n==================\n");
test( ch130, TSP_she130, 5, true );
test( a279, TSP_she279, 5, true );
test( p654, TSP_she654, 5, true );
printf("HORMIGAS COLONIA\n==================\n");
test( ch130, TSP_sch130, 5, true );
test( a279, TSP_sch279, 5, true );
test( p654, TSP_sch654, 5, true );
return 0;
}
RNG*
JKISS32::split() {
RNG* k = new JKISS32();
k->seed(this);
return k;
}
double operator()(Individual& ind, RNG& rng, EA& ea) {
int max_x = get<X_SIZE>(ea,10);
int max_y = get<Y_SIZE>(ea,10);
int grid_size = max_x * max_y;
vector<int> agent_pos (grid_size, -1);
vector<int> exec_order (grid_size);
double f=0.0;
// get the "prototype" phenotype (markov network):
typename EA::phenotype_type &N = ealib::phenotype(ind, ea);
// start with one agent...
vector<typename EA::phenotype_type> as; //
for (int q=0; q<get<NUM_START_AGENTS>(ea,1); q++) {
as.push_back(N); //grid_size, N); // my agents or networks
agent_pos[q] = q;
as[q].reset(rng.seed());
}
for (int i=0; i<grid_size; ++i) {
exec_order[i] = i;
}
std::vector< std::vector<int> > cell_color(grid_size, std::vector<int>(2, 0));
update_world_stigmergic_communication_N(get<WORLD_UPDATES>(ea,10), agent_pos, exec_order, as, cell_color, ind, rng, ea);
double f1_01 = 1.0;
double f2_11 = 1.0;
double f3_10 = 1.0;
// Compute fitness.
for (int xy = 0; xy<grid_size; xy++) {
// set the input states...
int agent_x = floor(xy / max_x);
int agent_y = xy % max_x;
int p = agent_pos[xy];
if (p == -1) {
continue;
}
if (agent_x < (floor(max_x) / 2 )) {
if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 0)) {
++f3_10;
}
} else if (agent_x >= (floor(max_x) / 2 )) {
if ((agent_x ==0) || (agent_y == 0) || (agent_x == (max_x -1)) || (agent_y == (max_y -1))) {
if (((as[p]).output(0) == 0) && ((as[p]).output(1) == 1)) {
++f1_01;
}
} else if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 1)) {
++f2_11;
}
}
}
//f = f1_01 * f2_11 * f3_10;
double e = f1_01 + f2_11 + f3_10 - 3;
f = pow(1.5, e);
return f;
}
double operator()(Individual& ind, RNG& rng, EA& ea) {
int max_x = get<X_SIZE>(ea,10);
int max_y = get<Y_SIZE>(ea,10);
int grid_size = max_x * max_y;
vector<int> agent_pos (grid_size, -1);
vector<int> exec_order (grid_size);
double f=0.0;
// get the "prototype" phenotype (markov network):
typename EA::phenotype_type &N = ealib::phenotype(ind, ea);
vector<typename EA::phenotype_type> as; //
for (int q=0; q<get<NUM_START_AGENTS>(ea,1); q++) {
as.push_back(N); //grid_size, N); // my agents or networks
agent_pos[q] = q;
as[q].reset(rng.seed());
}
for (int i=0; i<grid_size; ++i) {
exec_order[i] = i;
}
update_world_N(get<WORLD_UPDATES>(ea,10), agent_pos, exec_order, as, ind, rng, ea);
double f1_10 = 1.0;
double f1_01 = 1.0;
double f1_11 = 1.0;
double f2_10 = 1.0;
double f2_01 = 1.0;
double f2_11 = 1.0;
double f3_10 = 1.0;
double f3_01 = 1.0;
double f3_11 = 1.0;
// Compute fitness.
for (int xy = 0; xy<grid_size; xy++) {
// set the input states...
int agent_x = floor(xy / max_x);
int agent_y = xy % max_x;
int p = agent_pos[xy];
if (p == -1) {
continue;
}
if (agent_x <= (floor(max_x) / 3 )) {
if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 0)){
++f1_10;
} else if (((as[p]).output(0) == 0) && ((as[p]).output(1) == 1)){
++f1_01;
} else if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 1)){
++f1_11;
}
} else if ((agent_x > (floor(max_x) / 3)) && (agent_x < ((floor(max_x) / 3 * 2)))) {
if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 0)){
++f2_10;
} else if (((as[p]).output(0) == 0) && ((as[p]).output(1) == 1)){
++f2_01;
} else if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 1)){
++f2_11;
}
} else if (agent_x >= ((floor(max_x) / 3 * 2))) {
if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 0)){
++f3_10;
} else if (((as[p]).output(0) == 0) && ((as[p]).output(1) == 1)){
++f3_01;
} else if (((as[p]).output(0) == 1) && ((as[p]).output(1) == 1)){
++f3_11;
}
}
}
accumulator_set<double, stats<tag::max> > fs;
double fx = f1_10 * f2_01 * f3_11;
fs(fx);
fx = f1_10 * f2_11 * f3_01;
fs(fx);
fx = f1_01 * f2_10 * f3_11;
fs(fx);
fx = f1_01 * f2_11 * f3_10;
fs(fx);
fx = f1_11 * f2_01 * f3_10;
fs(fx);
fx = f1_11 * f2_10 * f3_01;
fs(fx);
f = boost::accumulators::max(fs);
return f;
}
double operator()(Individual& ind, RNG& rng, EA& ea) {
int max_x = get<X_SIZE>(ea,10);
int max_y = get<Y_SIZE>(ea,10);
int grid_size = max_x * max_y;
vector<int> agent_pos (grid_size, -1);
vector<int> exec_order (grid_size);
double f=0.0;
// get the "prototype" phenotype (markov network):
typename EA::phenotype_type &N = ealib::phenotype(ind, ea);
vector<typename EA::phenotype_type> as; //
for (int q=0; q<get<NUM_START_AGENTS>(ea,1); q++) {
as.push_back(N); //grid_size, N); // my agents or networks
agent_pos[q] = q;
as[q].reset(rng.seed());
}
for (int i=0; i<grid_size; ++i) {
exec_order[i] = i;
}
std::vector< std::vector<int> > cell_color(grid_size, std::vector<int>(2, 0));
std::vector<int> apop_count(grid_size,0);
update_world_stigmergic_communication_apop_N(get<WORLD_UPDATES>(ea,10), agent_pos, exec_order, as, cell_color, apop_count, ind, rng, ea);
int fit_func = get<BODYPLAN>(ea,0) ;
switch(fit_func) {
case 0:
f = body_plan0(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 1:
f = body_plan1(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 2:
f = body_plan2(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 3:
f = body_plan3(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 4:
f = body_plan4(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 5:
f = body_plan5(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 6:
f = body_plan6(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 7:
f = body_plan7(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 8:
f = body_plan8(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 9:
f = body_plan9(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 10:
f = body_plan10(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 11:
f = body_plan11(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 12:
f = body_plan12(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 13:
f = body_plan13(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 14:
f = body_plan14(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 15:
f = body_plan15(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 16:
f = body_plan16(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 17:
f = body_plan17(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 18:
f = body_plan18(grid_size, max_x, max_y, agent_pos, as, ea);
break;
case 19:
f = body_plan19(grid_size, max_x, max_y, agent_pos, as, ea);
break;
}
if (f == 0) {
f = 1;
}
return (f);
}
