int
main(int argc, char** argv)
{
cout << "Example 21\n\n";
cout << "This example shows various uses of the allele set object\n";
cout << "in combination with the real number genome.\n\n"; cout.flush();
// See if we've been given a seed to use (for testing purposes). When you
// specify a random seed, the evolution will be exactly the same each time
// you use that seed number.
unsigned int seed = 0;
for(int ii=1; ii<argc; ii++) {
if(strcmp(argv[ii++],"seed") == 0) {
seed = atoi(argv[ii]);
}
}
// First make a bunch of genomes. We'll use each one in turn for a genetic
// algorithm later on. Each one illustrates a different method of using the
// allele set object. Each has its own objective function.
// This genome is created using an array of allele sets. This means that each
// element of the genome will assume a value in its corresponding allele set.
// For example, since the first allele set is [0,10], the first element of the
// genome will be in [0,10]. Notice that you can add allele sets in many other
// ways than those shown.
GARealAlleleSetArray alleles4;
for(int j=1;j<34;j++)
{
alleles4.add(0,10);
}
GARealGenome genome4(alleles4, Objective4);
// Now that we have the genomes, create a parameter list that will be used for
// all of the genetic algorithms and all of the genomes.
GAParameterList params;
GASteadyStateGA::registerDefaultParameters(params);
params.set(gaNnGenerations, 500);
params.set(gaNpopulationSize, 110);
params.set(gaNscoreFrequency, 10);
params.set(gaNflushFrequency, 50);
params.set(gaNselectScores, (int)GAStatistics::AllScores);
params.parse(argc, argv, gaFalse);
// Now do a genetic algorithm for each one of the genomes that we created.
GASteadyStateGA ga4(genome4);
ga4.parameters(params);
ga4.set(gaNscoreFilename, "bog4.dat");
cout << "\nrunning ga number 4 (maximize each gene)..." << endl;
ga4.evolve();
cout << "the ga generated:\n" << ga4.statistics().bestIndividual() << endl;
return 0;
}
int
main(int argc, char** argv)
{
cout << "Example 21\n\n";
cout << "This example shows various uses of the allele set object\n";
cout << "in combination with the real number genome.\n\n"; cout.flush();
// See if we've been given a seed to use (for testing purposes). When you
// specify a random seed, the evolution will be exactly the same each time
// you use that seed number.
unsigned int seed = 0;
for(int ii=1; ii<argc; ii++) {
if(strcmp(argv[ii++],"seed") == 0) {
seed = atoi(argv[ii]);
}
}
// First make a bunch of genomes. We'll use each one in turn for a genetic
// algorithm later on. Each one illustrates a different method of using the
// allele set object. Each has its own objective function.
int length = 8;
// This genome uses an enumerated list of alleles. We explictly add each
// allele to the allele set. Any element of the genome may assume the value
// of any member of the allele set.
GARealAlleleSet alleles1;
alleles1.add(-10);
alleles1.add(0.1);
alleles1.add(1.0);
alleles1.add(10);
alleles1.add(100);
GARealGenome genome1(length, alleles1, Objective1);
// This genome uses a bounded set of continous numbers. The default arguments
// are INCLUSIVE for both the lower and upper bounds, so in this case the
// allele set is [0,1] and any element of the genome may assume a value [0,1].
GARealAlleleSet alleles2(0, 1);
GARealGenome genome2(length, alleles2, Objective2);
// Similar to the previous set, but this one has EXCLUSIVE bounds and we create
// the allele set explicitly (even though in this case
GARealAlleleSetArray alleles2a;
for(int i=0; i<length; i++)
alleles2a.add(0, 1, GAAllele::EXCLUSIVE, GAAllele::EXCLUSIVE);
GARealGenome genome2a(alleles2a, Objective2);
// Here we create a genome whose elements may assume any value in the interval
// [0.0, 10.0) discretized on the interval 0.5, i.e. the values 0.0, 0.5, 1.0,
// and so on up to but not including 10.0.
// Note that the default operators for the real genome are uniform initializer,
// gaussian mutator, and uniform crossover. Since gaussian is not the behavior
// we want for mutation, we assign the flip mutator instead.
GARealAlleleSet alleles3(0,10,0.5,GAAllele::INCLUSIVE,GAAllele::EXCLUSIVE);
GARealGenome genome3(length, alleles3, Objective3);
genome3.crossover(GARealUniformCrossover);
genome3.mutator(GARealSwapMutator);
// This genome is created using an array of allele sets. This means that each
// element of the genome will assume a value in its corresponding allele set.
// For example, since the first allele set is [0,10], the first element of the
// genome will be in [0,10]. Notice that you can add allele sets in many other
// ways than those shown.
GARealAlleleSetArray alleles4;
alleles4.add(0,10);
alleles4.add(50,100);
alleles4.add(-10,-5);
alleles4.add(-0.01,-0.0001);
alleles4.add(10000,11000);
GARealGenome genome4(alleles4, Objective4);
// Now that we have the genomes, create a parameter list that will be used for
// all of the genetic algorithms and all of the genomes.
GAParameterList params;
GASteadyStateGA::registerDefaultParameters(params);
params.set(gaNnGenerations, 500);
params.set(gaNpopulationSize, 110);
params.set(gaNscoreFrequency, 10);
params.set(gaNflushFrequency, 50);
params.set(gaNselectScores, (int)GAStatistics::AllScores);
params.parse(argc, argv, gaFalse);
// Now do a genetic algorithm for each one of the genomes that we created.
GASteadyStateGA ga1(genome1);
ga1.parameters(params);
ga1.set(gaNscoreFilename, "bog1.dat");
cout << "\nrunning ga number 1 (alternate allele(0) and allele(3))..."<<endl;
ga1.evolve(seed);
cout << "the ga generated:\n" << ga1.statistics().bestIndividual() << endl;
GASteadyStateGA ga2(genome2);
ga2.parameters(params);
ga2.set(gaNscoreFilename, "bog2.dat");
cout << "\nrunning ga number 2 (continuous descending order)..." << endl;
ga2.evolve();
cout << "the ga generated:\n" << ga2.statistics().bestIndividual() << endl;
GASteadyStateGA ga2a(genome2a);
ga2a.parameters(params);
ga2a.set(gaNscoreFilename, "bog2a.dat");
cout << "\nrunning ga number 2a (descending order, EXCLUSIVE)..." << endl;
ga2a.evolve();
cout << "the ga generated:\n" << ga2a.statistics().bestIndividual() << endl;
GASteadyStateGA ga3(genome3);
ga3.parameters(params);
ga3.set(gaNscoreFilename, "bog3.dat");
cout << "\nrunning ga number 3 (discretized ascending order)..." << endl;
ga3.evolve();
cout << "the ga generated:\n" << ga3.statistics().bestIndividual() << endl;
GASteadyStateGA ga4(genome4);
ga4.parameters(params);
ga4.set(gaNscoreFilename, "bog4.dat");
cout << "\nrunning ga number 4 (maximize each gene)..." << endl;
ga4.evolve();
cout << "the ga generated:\n" << ga4.statistics().bestIndividual() << endl;
return 0;
}
