?? gs.cc-old
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for (J=0; J < original_pop.num_individuals(); J++) {//debug allzero = allzero & (alloc[J] == (float)0.0);//debug }//debug if (allzero) {//debug (void)fprintf(logFile, "gs.cc: W A R N I N G ! all alloc variables are zero!\n"); //debug }//debug#endif // Permute the individuals#ifdef DEBUG2 (void)fprintf(logFile, "gs.cc: Permuting beginning: original_pop.num_individuals()= %d\n", original_pop.num_individuals()); //debug#endif for (i=0; i < original_pop.num_individuals(); i++) { temp_ordering = ordering[i]; assert(ordering[i] < original_pop.num_individuals());//debug temp_index = ignlgi()%(original_pop.num_individuals()); assert(ordering[temp_index] < original_pop.num_individuals());//debug ordering[i] = ordering[temp_index]; ordering[temp_index] = temp_ordering;#ifdef DEBUG2 //(void)fprintf(logFile, "gs.cc: permuting in sel_prop: ordering check: ordering[i=%d]= %d, ordering[temp_index=%d]= %d\n", ordering[i],i, ordering[temp_index], temp_index);//debug#endif }// endfor i // We might get some savings here if we sorted the individuals before calling // this routine for (i=0; (i < original_pop.num_individuals()) && (start_index < original_pop.num_individuals()); i++) { for (; (alloc[i] >= 1.0) && (start_index < original_pop.num_individuals()); alloc[i]-= 1.0) { new_pop[start_index] = original_pop[i]; // //new_pop[start_index].incrementAge(); // ++start_index; // } }#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc/void Genetic_Algorithm::"); //debug (void)fprintf(stderr, "selection_proportional(Population &original_pop, Individual *new_pop)\n"); //debug#endif i = 0; //#ifdef DEBUG2 int count = 0;//debug (void)fprintf(stderr, "gs.cc/beginning \"while(start_index < original_pop.num_individuals()) {\" loop\n"); //debug#endif // ??? start_index = 0; // gmm, 1998-07-13 ??? while (start_index < original_pop.num_individuals()) { //#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc:596/inside \"while(start_index(=%d) < original_pop.num_individuals()(=%d)) \" loop: count= %d\n", start_index, original_pop.num_individuals(), ++count); //debug#endif assert(ordering[i] < original_pop.num_individuals());//debug#ifdef DEBUG2 debug_ranf = ranf(); (void)fprintf(stderr, "gs.cc:599/inside debug_ranf= %.3f, alloc[ordering[i]]= %.3e, ordering[i]= %d, i= %d\n", debug_ranf, alloc[ordering[i]], ordering[i], i); // debug if (debug_ranf < alloc[ordering[i]]) {#else if (ranf() < alloc[ordering[i]]) { // non-debug#endif // not DEBUG2#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc:603/inside (debug_ranf < alloc[ordering[i]]) is true!\n"); //debug (void)fprintf(stderr, "gs.cc:604/inside about to increment start_index in: \"new_pop[start_index++] = original_pop[ordering[i]];\"; right now, start_index= %d\n", start_index); //debug#endif new_pop[start_index] = original_pop[ordering[i]]; // //new_pop[start_index].incrementAge(); // start_index++; //#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc:605/inside just incremented start_index, now start_index= %d\n", start_index); //debug#endif }// endif (ranf() < alloc[ordering[i]]) //#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc:608/inside i= %d, original_pop.num_individuals()= %d\n", i, original_pop.num_individuals()); //debug (void)fprintf(stderr, "gs.cc:609/inside about to \"i = (i+1)%%original_pop.num_individuals();\"\n"); //debug#endif i = (i+1)%original_pop.num_individuals(); //#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc:611/inside just done \"i = (i+1)%%original_pop.num_individuals();\"\n"); //debug (void)fprintf(stderr, "gs.cc:612/inside i= %d _____________________________________________________\n\n", i); //debug allzero = 1;//debug for (J=0; J < original_pop.num_individuals(); J++) {//debug allzero = allzero & (alloc[J] == (float)0.0);//debug }//debug if (allzero) {//debug (void)fprintf(logFile, "gs.cc: W A R N I N G ! all alloc variables are zero!\n"); //debug }//debug#endif }// endwhile (start_index < original_pop.num_individuals()) //#ifdef DEBUG2 (void)fprintf(stderr, "gs.cc/finished \"while(start_index < original_pop.num_individuals()) \" loop\n"); //debug#endif}/* Probabilistic Binary Tournament Selection * * This type of tournament selection was described in Goldberg and Deb (1991), * and performs the same type of selection as is seen in linear rank * selection. Two individuals are chosen at random, and the better individual * is selected with probability P, 0.5 <= P <= 1. If we let 2P = C, then * we see that the expected number of samples generated by rank r_i is * * N * [2P - 2(2P-1) r_i] , 1 >= P >= 0.5 * * We can again parameterize this ranking with K, the relative probability * between the best and worst individual. Since 2P = C, * P = K/(1+K). */void Genetic_Algorithm::selection_tournament(Population &original, Individual *new_pop){ register int i = 0, start_index = 0; int temp_ordering, temp_index;#ifdef DEBUG (void)fprintf(logFile, "gs.cc/void Genetic_Algorithm::"); (void)fprintf(logFile, "selection_tournament(Population &original, Individual *new_pop)\n");#endif /* DEBUG */ original.msort(original.num_individuals()); for (i=0; i<original.num_individuals(); i++) { alloc[i] = original.num_individuals()*(2*tournament_prob - i*(4*tournament_prob - 2)); } for (i=0; (i < original.num_individuals()) && (start_index < original.num_individuals()); i++) { for (; (alloc[i] >= 1.0) && (start_index < original.num_individuals()); alloc[i] -= 1.0) { new_pop[start_index++] = original[i]; } } for (i=0; i < original.num_individuals(); i++) { temp_ordering = ordering[i]; temp_index = ignlgi()%original.num_individuals(); ordering[i] = ordering[temp_index]; ordering[temp_index] = temp_ordering; } i = 0; while (start_index < original.num_individuals()) { if (ranf() < alloc[ordering[i]]) { new_pop[start_index++] = original[ordering[i]]; } i = (i+1)%original.num_individuals(); }}Individual *Genetic_Algorithm::selection(Population &solutions){ Individual *next_generation;#ifdef DEBUG (void)fprintf(logFile, "gs.cc/Individual *Genetic_Algorithm::selection(Population &solutions)\n");#endif /* DEBUG */ next_generation = new Individual[solutions.num_individuals()]; set_worst(solutions); switch(s_mode) { case Proportional: selection_proportional(solutions, next_generation); break; case Tournament: selection_tournament(solutions, next_generation); break; case Boltzmann: (void)fprintf(logFile,"gs.cc/Unimplemented Selection Method - using proportional\n"); selection_proportional(solutions, next_generation); break; default: (void)fprintf(logFile,"gs.cc/Unknown Selection Mode!\n"); } return(next_generation);}// For right now global search is taken to be a GAint Genetic_Algorithm::search(Population &solutions){ int i, outputEveryNgens = 1; unsigned int oldest = 0, oldestIndividual = 0, fittestIndividual = 0; double fittest = BIG; struct tms tms_genStart; struct tms tms_genEnd; Clock genStart; Clock genEnd;#ifdef DEBUG (void)fprintf(logFile, "gs.cc/int Genetic_Algorithm::search(Population &solutions)\n");#endif /* DEBUG */ genStart = times( &tms_genStart );#ifdef DEBUG3 (void)fprintf(logFile,"[Pre-Mapping] (solutions)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%d ", solutions[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ for (i=0; i<solutions.num_individuals(); i++) { solutions[i].mapping(); } #ifdef DEBUG3 (void)fprintf(logFile,"[Pre-Selection] (solutions)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%ld ", solutions[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ // Perform selection Population newPop(solutions.num_individuals(), selection(solutions));#ifdef DEBUG3 (void)fprintf(logFile,"[Pre-Crossover] (newPop)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%ld ", newPop[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ // Perform crossover crossover(newPop);#ifdef DEBUG3 (void)fprintf(logFile,"[Pre-Mutation] (newPop)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%ld ", newPop[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ // Perform mutation mutation(newPop);#ifdef DEBUG3 (void)fprintf(logFile,"[Pre-Elitism] (newPop)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%ld ", newPop[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ // Copy the n best individuals to the next population, if the elitist flag is set. if (elitism>0) { solutions.msort(elitism); for (i=0; i<elitism; i++) { newPop[solutions.num_individuals()-1-i] = solutions[i]; } }#ifdef DEBUG3 (void)fprintf(logFile,"[Pre-UpdateCurrentGeneration] (newPop)\n"); for (i=0; i<solutions.num_individuals(); i++) { (void)fprintf(logFile,"%ld ", newPop[i].age); } (void)fprintf(logFile,"\n");#endif /* DEBUG3 */ // Update current generation... solutions = newPop; generations++; // Increment age of surviving individuals... for (i=0; i<solutions.num_individuals(); i++) { solutions[i].incrementAge(); } if (generations%outputEveryNgens == 0) { oldest = 0L; fittest = BIG; for (i=0; i<solutions.num_individuals(); i++) { if (solutions[i].age >= oldest) { oldest = solutions[i].age; oldestIndividual = i; } if (solutions[i].value(Normal_Eval) <= fittest) { fittest = solutions[i].value(Normal_Eval); fittestIndividual = i; } } if (outputEveryNgens > 1) {#ifndef DEBUG3 (void)fprintf(logFile,"Generation: %3u, Oldest individual's energy: %.3f; Lowest energy: %.3f; Time taken for last %d generations: ", generations, solutions[oldestIndividual].value(Normal_Eval), solutions[fittestIndividual].value(Normal_Eval), outputEveryNgens);#else (void)fprintf(logFile,"Generation: %3u, Oldest individual: %u/%u, age: %uld, energy: %.3f; Lowest energy individual: %u/%u, age: %uld, energy: %.3f; Time taken for last %d generations: ", generations, oldestIndividual+1L, solutions.num_individuals(), solutions[oldestIndividual].age, solutions[oldestIndividual].value(Normal_Eval), fittestIndividual+1L, solutions.num_individuals(), solutions[fittestIndividual].age, solutions[fittestIndividual].value(Normal_Eval), outputEveryNgens);#endif /* DEBUG3 */ } else {#ifndef DEBUG3 (void)fprintf(logFile,"Generation: %3u, Oldest individual's energy: %.3f; Lowest energy: %.3f; Time taken: ", generations, solutions[oldestIndividual].value(Normal_Eval), solutions[fittestIndividual].value(Normal_Eval));#else (void)fprintf(logFile,"Generation: %3u, Oldest individual: %u/%u, age: %uld, energy: %.3f; Lowest energy individual: %u/%u, age: %uld, energy: %.3f; Time taken: ", generations, oldestIndividual+1L, solutions.num_individuals(), solutions[oldestIndividual].age, solutions[oldestIndividual].value(Normal_Eval), fittestIndividual+1L, solutions.num_individuals(), solutions[fittestIndividual].age, solutions[fittestIndividual].value(Normal_Eval));#endif /* DEBUG3 */ } genEnd = times( &tms_genEnd ); timesyshms( genEnd - genStart, &tms_genStart, &tms_genEnd ); //genStart = times( &tms_genStart ); } return(0);}?? 快捷鍵說明
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