EvolutionaryAlgorithm::EvolutionaryAlgorithm(vector<CoinType> & newCoinTypes, int newPopulationSize,
int newTargetValue, int newNumberOfGenerations, double newElitismBias, int newNumberOfGenes, double newMutationRate, bool newStopAtGoal, int newAgeLimit,int newTournamentSize)
{
setStopAtGoal(newStopAtGoal);
setMutationRate(newMutationRate);
setNumberOfGenes(newNumberOfGenes);
setEilitismBias(newElitismBias);
setNumberOfGenerations(newNumberOfGenerations);
setPopulationSize(newPopulationSize);
setTargetValue(newTargetValue);
setCoinTypes(newCoinTypes);
setAgeLimit(newAgeLimit);
setTournamentSize(newTournamentSize);
setNumberOfChildren();
}
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;
}
// FIX - too long (separate into more than one function)
// FIX - Make a function that prints out its usage (help function)
void initParameters(int argc, char ** argv){
char * arch_filename = DEFAULT_ARCH_FILENAME;
char * dfg_filename = DEFAULT_DFG_FILENAME;
char * prr_filename = DEFAULT_PRR_FILENAME;
int seed = randSeed();
int c;
opterr = 0;
while((c = getopt(argc, argv, "a:b:c:d:f:g:h:m:n:o:p:r:s:t:Vvw:")) != -1){
switch(c){
case 'a':
arch_filename = optarg;
break;
case 'b':
if(strncasecmp("one-point", optarg, strlen(optarg)) == 0 ||
strncasecmp("one_point", optarg, strlen(optarg)) == 0 ||
strncasecmp("one point", optarg, strlen(optarg)) == 0 )
crossover_type = 1;
else if(strncasecmp("two-point", optarg, strlen(optarg)) != 0 &&
strncasecmp("two_point", optarg, strlen(optarg)) != 0 &&
strncasecmp("two point", optarg, strlen(optarg)) != 0 ){
fprintf(stderr, "Unknown crossover option : %s\n", optarg);
exit(1);
}
break;
case 'c':
setCrossoverRate(atof(optarg));
break;
case 'd':
dfg_filename = optarg;
break;
case 'f':
setFitnessFunction(optarg);
break;
case 'g':
MAX_NUM_GENERATIONS = atoi(optarg);
break;
case 'h':
setSetupIndex(atoi(optarg));
break;
case 'm':
setMutationRate(atof(optarg));
break;
case 'n':
if(strncasecmp("random", optarg, strlen(optarg)) == 0)
mutation_type = 2;
else if(strncasecmp("rotationally", optarg, strlen(optarg)) != 0){
fprintf(stderr, "Unknown mutation option : %s\n", optarg);
exit(1);
}
break;
case 'p':
setPopSize(atoi(optarg));
break;
case 'r':
if(strncasecmp("keep-best", optarg, strlen(optarg)) == 0 ||
strncasecmp("keep_best", optarg, strlen(optarg)) == 0 ||
strncasecmp("keep best", optarg, strlen(optarg)) == 0)
replacement_type = 2;
else if(strncasecmp("all", optarg, strlen(optarg)) != 0){
fprintf(stderr, "Unknown replacement option : %s\n", optarg);
exit(1);
}
break;
case 's':
if(strncasecmp("random", optarg, strlen(optarg)) == 0)
selection_type = 2;
else if(strncasecmp("tournament", optarg, strlen(optarg)) != 0){
fprintf(stderr, "Unknown selection option : %s\n", optarg);
exit(1);
}
break;
case 't':
seed = atoi(optarg);
break;
case 'V':
case 'v':
fprintf(stdout, "Offline Scheduler version 2.0 (GA + Napoleon + rcSimulator)\n");
#ifdef DEBUG
fprintf(stdout, "Using the DEBUG build\n\n");
#endif
#ifdef VERBOSE
fprintf(stdout, "Using the VERBOSE build\n\n");
#endif
#ifdef STATS
fprintf(stdout, "Using the STATS build\n\n");
#endif
fprintf(stdout, "Please see https://github.com/Aalwattar/GA for more information\n");
exit(0);
case 'w':
setRuntimeWeight(atof(optarg));
break;
case ':':
fprintf(stderr, "Option -%c requires an operand\n", optopt);
break;
case '?':
fprintf(stderr, "Unrecognized option: -%c\n", optopt);
default:
exit(1);
}
}
if(optind < argc){
printf("Non-option argument %s\n", argv[optind]);
exit(1);
}
// FIX - Check the return value
seedRandGenerator(seed);
if(initScheduler(arch_filename, dfg_filename, prr_filename) == EXIT_FAILURE)
exit(1);
if(POP_SIZE == 0){
POP_SIZE = getNumNodes();
}
fprintf(stdout, "Parameters:\n");
fprintf(stdout, "\tFitness Function = %s\n\n", getFitnessFunction());
fprintf(stdout, "\tPopulation Size = %d\n", POP_SIZE);
fprintf(stdout, "\tNumber of Generations = %d\n\n", MAX_NUM_GENERATIONS);
fprintf(stdout, "\tMutation Rate = %.4lf\n", getMutationRate());
fprintf(stdout, "\tCrossover Rate = %.4lf\n", getCrossoverRate());
fprintf(stdout, "\tRuntime Weight = %.4lf\n", getRuntimeWeight());
fprintf(stdout, "\tPower Weight = %.4lf\n\n", 1.0 - getRuntimeWeight());
if(crossover_type == 1)
fprintf(stdout, "\tCrossover Algorithm = One point Crossover\n");
else
fprintf(stdout, "\tCrossover Algorithm = Two point Crossover\n");
if(mutation_type == 1)
fprintf(stdout, "\tMutation Algorithm = Rotational Mutation\n");
else
fprintf(stdout, "\tMutation Algorithm = Random Mutation\n");
if(selection_type == 1)
fprintf(stdout, "\tSelection Algorithm = Tournament Selection\n");
else
fprintf(stdout, "\tSelection Algorithm = Random Selection\n");
if(replacement_type == 1)
fprintf(stdout, "\tReplacement Algorithm = Replace All\n\n");
else
fprintf(stdout, "\tReplacement Algorithm = Keep Best\n\n");
// FIX - make this dynamic
fprintf(stdout, "\tSeed = %d\n", seed);
fprintf(stdout, "\tPercent seeding = %.4lf\n", (1.0 - PERCENT_POP_RANDOM));
}
