userdefinables.cpp

遗传算法（Genetic Algorithm）是一类借鉴生物界的进化规律（适者生存

  case NoConstraints:
  case Tournament:
    // no extra parameters
    break;
  case Penalty:
    {
      // penalty function
      if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
	printf("Using linear penalty be default\n");
	globalSetup->penaltyFunction = Linear;
      }
      else if (strcmp("Linear", pToken) == 0) {
	globalSetup->penaltyFunction = Linear;
      }
      else if (strcmp("Quadratic", pToken) == 0) {
	globalSetup->penaltyFunction = Quadratic;
      }
      else {
	fclose(fInput);
	printf("Error! valid penalty function methods are: Linear and Quadratic\n");
	exit(1);
      }
    }
    break;
  default:
    {
      fclose(fInput);
      printf("Error! invalid constraint-handling method parameter\n");
      exit(1);
    }
    break;
  }

  // local search method
  if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
    fclose(fInput);
    printf("Error in the input file, please refer to the documentation\n");
    exit(1);
  }
  if(strcmp("default", pToken) == 0)
  {
    printf("Using no local search by default\n");
    globalSetup->localSearchMethod = NoLocalSearch;
  }
  // local search method
  else if (strcmp("NoLocalSearch", pToken) == 0) {
    globalSetup->localSearchMethod = NoLocalSearch;
  }
  else if (strcmp("SimplexSearch", pToken) == 0) {
    globalSetup->localSearchMethod = SimplexSearch;
  }
  else {
    fclose(fInput);
    printf("Error! valid local search methods are: NoLocalSearch and SimplexSearch\n");
    exit(1);
  }
  // check local search method
  if ((globalSetup->localSearchMethod != NoLocalSearch) && (globalSetup->gaType == NSGA)) {
    fclose(fInput);
    printf("Error! cannot use local search with NSGA\n");
    exit(1);
  }
  switch(globalSetup->localSearchMethod) {
  case NoLocalSearch:
    // no extra parameters
    break;
  case SimplexSearch:
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Using default tolerance of 0.001\n");
      globalSetup->maxLocalTolerance = 1.0E-3;
    }
    else
      globalSetup->maxLocalTolerance = atof(pToken);
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Setting maximum local search evaluations to 20\n");
      globalSetup->maxLocalEvaluations = 20;
    }
    else
      globalSetup->maxLocalEvaluations = atoi(pToken);
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Using default local penalty parameter of 1.0\n");
      globalSetup->initialLocalPenaltyParameter = 1.0;
    }
    else
      globalSetup->initialLocalPenaltyParameter = atof(pToken);
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Using default local update parameter of 2.0\n");
      globalSetup->localUpdateParameter = 2.0;
    }
    else
      globalSetup->localUpdateParameter = atof(pToken);
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Using default Lamarckian probability of 0.15\n");
      globalSetup->lamarckianProbability = 0.15;
    }
    else
      globalSetup->lamarckianProbability = atof(pToken);
    if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
      printf("Using default overall local search probability of 0.5\n");
      globalSetup->localSearchProbability = 0.5;
    }
    else
      globalSetup->localSearchProbability = atof(pToken);
    break;
  default:
    {
      fclose(fInput);
      printf("Error! invalid local search parameters\n");
      exit(1);
    }
    break;
  }
  
  // stopping criteria
  
  // noOfStoppingCriterias
  if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
    fclose(fInput);
    printf("Error in the input file, please refer to the documentation\n");
    exit(1);
  }
  if(strcmp("default", pToken) == 0)
  {
    printf("Using no extra stopping criterias by default.\n");
    globalSetup->noOfStoppingCriterias = 0;
  }
  // the number can't be less than zero
  else if ((globalSetup->noOfStoppingCriterias = atoi(pToken)) < 0) {
    fclose(fInput);
    printf("Error! number of stopping criterias must be > 0\n");
    exit(1);
  }
  else if (globalSetup->noOfStoppingCriterias == 0) {
    if ((strlen(pToken) != 1) || (*pToken != '0')) {
      fclose(fInput);
      printf("Error! number of stopping criterias must be a number!\n");
      exit(1);
    }
  }

  // allocate memory
  if(globalSetup->noOfStoppingCriterias > 0) {
    // number of generation window
    if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
      printf("The default window size used in stopping critiria is: 5.\n");
      globalSetup->genNumWindow = 5;
    }
    else
      globalSetup->genNumWindow = atoi(pToken);

    // the number should be greater than zero  
    if (globalSetup->genNumWindow <= 0) {
      fclose(fInput);
      printf("Error! window size used in stopping critieria must be > 0\n");
      exit(1);
    }
    globalSetup->otherStoppingCriteria = new StoppingCriterias[globalSetup->noOfStoppingCriterias];
    globalSetup->stoppingParameter = new double[globalSetup->noOfStoppingCriterias];

    for (ii = 0 ; ii < globalSetup->noOfStoppingCriterias ; ii++) {
      // read a line
      if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
	fclose(fInput);
	printf("Error in the input file, Please see the documentation.\n");
	exit(1);
      }
      // stopping criterion type
      if (strcmp("NoOfEvaluations", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = NoOfEvaluations;
      }
      else if (strcmp("FitnessVariance", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = FitnessVariance;
      }
      else if (strcmp("AverageFitness", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = AverageFitness;
      }
      else if (strcmp("AverageObjective", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = AverageObjective;
      }
      else if (strcmp("ChangeInBestFitness", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInBestFitness;
      }
      else if (strcmp("ChangeInAvgFitness", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInAvgFitness;
      }
      else if (strcmp("ChangeInFitnessVar", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInFitnessVar;
      }
      else if (strcmp("ChangeInBestObjective", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInBestObjective;
      }
      else if (strcmp("ChangeInAvgObjective", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInAvgObjective;
      }
      else if (strcmp("NoOfFronts", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = NoOfFronts;
      }
      else if (strcmp("NoOfGuysInFirstFront", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = NoOfGuysInFirstFront;
      }
      else if (strcmp("ChangeInNoOfFronts", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = ChangeInNoOfFronts;
      }
      else if (strcmp("BestFitness", pToken) == 0) {
	globalSetup->otherStoppingCriteria[ii] = BestFitness;
      }
      else {
	fclose(fInput);
	printf("Error! invalid stopping criteria parameter\n");
	exit(1);
      }

      // check criteria
      switch(globalSetup->otherStoppingCriteria[ii]) {
      case NoOfEvaluations:
      case FitnessVariance:
	// 1 - nothing to check
	break;
	
      case AverageFitness:
      case AverageObjective:
      case ChangeInBestFitness:
      case ChangeInAvgFitness:
      case ChangeInFitnessVar:
      case ChangeInBestObjective:
      case ChangeInAvgObjective:
      case BestFitness:
	// 2 - SGA w/o MM only
	if ((globalSetup->gaType != SGA) || (globalSetup->nichingType != NoNiching)) {
	  fclose(fInput);
	  printf("Cannot use the following stopping critier with NSGA or when Niching is used:\n");
	  printf("\t AverageFitness\n\t AverageObjective\n");
	  printf("\t ChangeInBestFitness\n\t ChangeInAvgFitness\n");
	  printf("\t ChangeInFitnessVar\n\t ChangeInBestObjective\n");
	  printf("\t ChangeInAvgObjective\n\t BestFitness\n");
	  printf("Error! invalid choice of stopping criteria");
	  exit(1);
	}
	break;
      case NoOfFronts:
      case NoOfGuysInFirstFront:
      case ChangeInNoOfFronts:
	// 3 - NSGA only
	if (globalSetup->gaType != NSGA) {
	  fclose(fInput);
	  printf("Error! following stopping criteria can be used only with NSGA:\n");
	  printf("\t NoOfFronts\n\t NoOfGuysInFirstFront\n\t ChangeInNoOfFronts\n");
	  exit(1);
	}
	break;
	
      default:
	{
	  fclose(fInput);
	  printf("Error! invalid stopping criteria\n");
	  exit(1);
	}
	break;
      }
      // parameter
      if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
	fclose(fInput);
	printf("Error! invalid stopping criteria parameter\n");
	exit(1);
      }
      ((double*)globalSetup->stoppingParameter)[ii] = atof(pToken);
    }
  }
  // Initial population generation
  
  // Load population
  if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
    fclose(fInput);
    printf("Error in the input file, please refer to the documentation\n");
    exit(1);
  }
  if(strcmp("default", pToken) == 0)
  {
    printf("Using random initialization by default.\n");
    globalSetup->loadPopulation = false;
    globalSetup->evaluateAgain = true;
  }
  // Check if the load population is 0 or 1
  else {
    // load the population from a file
    if(atoi(pToken) == 1) {
      globalSetup->loadPopulation = true;
      
      globalSetup->populationFileName = new char[256];
      // Read the name of the file to load the population from
      if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
	fclose(fInput);
	printf("Error! invalid file name to load the population from, see documentation for more details.\n");
	exit(1);
      }
      strcpy(globalSetup->populationFileName, pToken);

      // Evaluate the population or not
      if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
	printf("Evaluating the initial population by default.\n");
	globalSetup->evaluateAgain = true;
      }
      else if(atoi(pToken) == 0) {
	globalSetup->evaluateAgain = false;
      }
      else
	globalSetup->evaluateAgain = true;      
    }
    // Use random initialization
    else {
      globalSetup->loadPopulation = false;
      globalSetup->evaluateAgain = true;
    }
  }

  // Save population
  if ((pToken = readOneLine(caBuf, ciBufSize, fInput)) == NULL) {
    fclose(fInput);
    printf("Error in the input file, please refer to the documentation\n");
    exit(1);
  }
  if(strcmp("default", pToken) == 0) {
    printf("Saving the evaluated individuals to a file by default.\n");
    globalSetup->savePopulation = true;
    globalSetup->saveEvalSolutions = new char[256];
    printf("Enter the filename you want to save the population to.\n");
    fflush(stdout);
    scanf("%s", globalSetup->saveEvalSolutions);
  }
  // Check if the save population is 0 or 1
  else {
    fflush(stdout);
    // Save the population to a file
    if(atoi(pToken) == 1) {
      globalSetup->savePopulation = true;
      // Filename to save the evaluated solutions to.
      globalSetup->saveEvalSolutions = new char[256];
      if ((pToken = strtok(NULL, BLANK_STR)) == NULL) {
	fclose(fInput);
	printf("Error in the input file, please refer to the documentation\n");
	exit(1);
      }
      strcpy(globalSetup->saveEvalSolutions, pToken);
    }
  }
  fclose(fInput);
  fflush(stdout);
  
  if(globalSetup->savePopulation) {
    fOutput = fopen(globalSetup->saveEvalSolutions, "w");
    fprintf(fOutput, "%% ");
    for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {
      fprintf(fOutput, "var #%d\t", ii);
    }  
    for(ii = 0; ii < globalSetup->finalNoOfObjectives; ii++) {
      fprintf(fOutput, "obj #%d\t", ii);
    }
    if(globalSetup->finalNoOfConstraints > 0) {
      for(ii = 0; ii < globalSetup->finalNoOfConstraints; ii++) {
	fprintf(fOutput, "const #%d\t", ii);
      }
      fprintf(fOutput, "penalty");
    }
    fprintf(fOutput, "\n");
    fclose(fOutput);
  }

  GA ga;
  if (globalSetup->gaType == SGA) {
    while(ga.generate());
  }
  else {
    while(ga.nsgaGenerate());
  }

  delete [](globalSetup->penaltyWeights);
  delete [](globalSetup->variableTypes);
  for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {
    delete [](globalSetup->variableRanges[ii]);
  }  
  delete [](globalSetup->variableRanges);
  delete [](globalSetup->typeOfOptimizations);
  if((globalSetup->selectionType == TournamentWR)||(globalSetup->selectionType == TournamentWOR)||(globalSetup->selectionType == Truncation)) {
    delete [](int*)(globalSetup->selectionParameters);
  }
  if((globalSetup->xOverType==Uniform)||(globalSetup->xOverType == SBX))
    delete [](double*)(globalSetup->xOverParameters);
  if(globalSetup->mutationType == Polynomial)
    delete [](int*)(globalSetup->mutationParameters);
  else if(globalSetup->mutationType == Genewise)
    delete [](double*)(globalSetup->mutationParameters);
  if(globalSetup->nichingType == RTS)
    delete [](int*)(globalSetup->nichingParameters);
  if(globalSetup->nichingType == Sharing)
    delete [](double*)(globalSetup->nichingParameters);
  if(globalSetup->scalingMethod == SigmaScaling)
    delete [](double*)(globalSetup->scalingParameters);
  if(globalSetup->noOfStoppingCriterias > 0) {
    delete [] globalSetup->otherStoppingCriteria;
    delete [](double*)(globalSetup->stoppingParameter);
  }
  if(globalSetup->loadPopulation)
    delete [](globalSetup->populationFileName);
  if(globalSetup->savePopulation)
    delete [](globalSetup->saveEvalSolutions);

  return 1;
}