int main(void){
struct parameters *params = NULL;
struct chromosome *chromoA = NULL;
struct chromosome *chromoB = NULL;
struct chromosome *chromoC = NULL;
struct dataSet *trainingData = NULL;
double testInputs[NUMINPUTS];
params = initialiseParameters(NUMINPUTS, NUMNODES, NUMOUTPUTS, ARITY);
addNodeFunction(params, "add,sub,mul,sq,cube,sin");
trainingData = initialiseDataSetFromFile("./dataSets/symbolic.data");
chromoA = initialiseChromosome(params);
chromoB = initialiseChromosome(params);
setChromosomeFitness(params, chromoA, trainingData);
mutateChromosome(params,chromoA);
copyChromosome(chromoB, chromoA);
removeInactiveNodes(chromoB);
printf("chromoA with inactive nodes.\n");
printChromosome(chromoA, 0);
printf("chromoB without inactive nodes.\n");
printChromosome(chromoB, 1);
saveChromosome(chromoB, "chromoB.chromo");
chromoC = initialiseChromosomeFromFile("chromoB.chromo");
testInputs[0] = 3;
executeChromosome(chromoC, testInputs);
printf("Applied input: %f\n", testInputs[0]);
printf("Generated output: %f\n", getChromosomeOutput(chromoC, 0));
freeChromosome(chromoA);
freeChromosome(chromoB);
freeChromosome(chromoC);
freeDataSet(trainingData);
freeParameters(params);
return 0;
}
int main(void){
struct parameters *params = NULL;
struct chromosome *chromo = NULL;
int numInputs = 1;
int numNodes = 20;
int numOutputs = 1;
int nodeArity = 5;
int numGens = 25000;
double targetFitness = 0.5;
int updateFrequency = 500;
double weightRange = 5;
params = initialiseParameters(numInputs, numNodes, numOutputs, nodeArity);
setTargetFitness(params, targetFitness);
setUpdateFrequency(params, updateFrequency);
setConnectionWeightRange(params, weightRange);
setCustomFitnessFunction(params, sinWave, "sinWave");
addNodeFunction(params, "tanh,softsign");
chromo = runCGP(params, NULL, numGens);
printChromosome(chromo, 1);
freeChromosome(chromo);
freeParameters(params);
return 0;
}
// Runs a full round of experiments
void performExperiments(struct parameters* params,
unsigned int nGenerations,
unsigned int nRepeats,
const std::string& nodeFunctions) {
setCustomFitnessFunction(params, fitness, "PocMan Level 1");
struct chromosome* bestChromosome = nullptr;
double bestFitness = DBL_MAX;
std::vector<double> fitnesses;
std::vector<double> durations;
fitnesses.reserve(nRepeats);
durations.reserve(nRepeats);
for (unsigned int i = 0; i < nRepeats; i++) {
// Run CGP, record the results
auto start = std::chrono::steady_clock::now();
struct chromosome* chromo = runCGP(params, nullptr, nGenerations);
auto end = std::chrono::steady_clock::now();
double fitness = getChromosomeFitness(chromo);
fitnesses.push_back(fitness);
std::chrono::duration<double> diff = end - start;
double duration = diff.count();
durations.push_back(duration);
// Should we keep this chromosome?
if (fitness < bestFitness) {
if (bestChromosome != nullptr)
freeChromosome(bestChromosome);
bestChromosome = chromo;
bestFitness = fitness;
} else {
freeChromosome(chromo);
}
}
// Stats!
double meanFitness, stdevFitness, minFitness, maxFitness;
std::tie(meanFitness, stdevFitness, minFitness, maxFitness) = calculateStats(fitnesses);
double meanDuration, stdevDuration, minDuration, maxDuration;
std::tie(meanDuration, stdevDuration, minDuration, maxDuration) = calculateStats(durations);
// Logging!
log("### PARAMETERS\n");
log(parametersToString(params));
log("generations: " + std::to_string(nGenerations) + "\n");
log("\n--------------------\n\n");
log("### FITNESS VALUES\n");
for (unsigned int i = 0; i < nRepeats; i++)
log("(" + std::to_string(i + 1) + "):\t" + std::to_string(fitnesses[i]) + "\n");
log("\n--------------------\n\n");
log("### STATISTICS (FITNESS)\n");
log("mean : " + std::to_string(meanFitness) + "\n");
log("std : " + std::to_string(stdevFitness) + "\n");
log("rng : " + std::to_string(minFitness) +":" + std::to_string(maxFitness) + "\n");
log("\n--------------------\n\n");
log("### DURATION VALUES\n");
for (unsigned int i = 0; i < nRepeats; i++)
log("(" + std::to_string(i + 1) + "):\t" + std::to_string(durations[i]) + "\n");
log("\n--------------------\n\n");
log("### STATISTICS (DURATIONS)\n");
log("mean : " + std::to_string(meanDuration) + "\n");
log("std : " + std::to_string(stdevDuration) + "\n");
log("rng : " + std::to_string(minDuration) + ":" + std::to_string(maxDuration) + "\n");
log("\n--------------------\n\n");
log("### BEST CHROMOSOME\n");
log(chromosomeToString(bestChromosome, 0));
log("\n--------------------\n\n");
log("### GAMEPLAY TRACE\n");
log(controller.generateGameTrace(bestChromosome));
}
int main(void){
int i, gen;
struct parameters *params = NULL;
struct chromosome *population[POPULATIONSIZE];
struct chromosome *fittestChromosome = NULL;
struct dataSet *trainingData = NULL;
double targetFitness = 0;
int maxGens = 10000;
params = initialiseParameters(NUMINPUTS, NUMNODES, NUMOUTPUTS, ARITY);
addNodeFunction(params, "or,nor,and,nand");
/*setTargetFitness(params, targetFitness);*/
setMutationType(params, "probabilistic");
setMutationRate(params, 0.08);
trainingData = initialiseDataSetFromFile("./examples/parity3bit.data");
for(i=0; i<POPULATIONSIZE; i++){
population[i] = initialiseChromosome(params);
}
fittestChromosome = initialiseChromosome(params);
/* for the number of allowed generations*/
for(gen=0; gen<maxGens; gen++){
/* set the fitnesses of the population of chromosomes*/
for(i=0; i<POPULATIONSIZE; i++){
setChromosomeFitness(params, population[i], trainingData);
}
/* copy over the last chromosome to fittestChromosome*/
copyChromosome(fittestChromosome, population[POPULATIONSIZE - 1]);
/* for all chromosomes except the last*/
for(i=0; i<POPULATIONSIZE-1; i++){
/* copy ith chromosome to fittestChromosome if fitter*/
if(getChromosomeFitness(population[i]) < getChromosomeFitness(fittestChromosome)){
copyChromosome(fittestChromosome, population[i]);
}
}
/* termination condition*/
if(getChromosomeFitness(fittestChromosome) <= targetFitness){
break;
}
/* set the first member of the population to be the fittest chromosome*/
copyChromosome(population[0], fittestChromosome);
/* set remaining member of the population to be mutations of the
fittest chromosome*/
for(i=1; i<POPULATIONSIZE; i++){
copyChromosome(population[i], fittestChromosome);
mutateChromosome(params, population[i]);
}
}
printf("gen\tfitness\n");
printf("%d\t%f\n", gen, getChromosomeFitness(fittestChromosome));
for(i=0; i<POPULATIONSIZE; i++){
freeChromosome(population[i]);
}
freeChromosome(fittestChromosome);
freeDataSet(trainingData);
freeParameters(params);
return 0;
}
