gabasega.c

这是一个面向对象的GA遗传算法库GAlib: A C++ Library of Genetic Algorithm Components)

const GAParameterList&
GAGeneticAlgorithm::parameters(int& argc, char **argv, GABoolean flag){
  params.parse(argc, argv, flag);	// get the args we understand
  for(int i=0; i<params.size(); i++)
    setptr(params[i].fullname(), params[i].value());
  return params;
}

#ifdef GALIB_USE_STREAMS
const GAParameterList&
GAGeneticAlgorithm::parameters(const char* filename, GABoolean flag){
  params.read(filename, flag);
  for(int i=0; i<params.size(); i++)
    setptr(params[i].fullname(), params[i].value());
  return params;
}

const GAParameterList&
GAGeneticAlgorithm::parameters(STD_ISTREAM& is, GABoolean flag){
  params.read(is, flag);
  for(int i=0; i<params.size(); i++)
    setptr(params[i].fullname(), params[i].value());
  return params;
}
#endif

// Return 0 if everything is OK, non-zero if error.  If we did not set anything
// then we return non-zero (this is not an error, but we indicate that we did
// not do anything).
//   The set method must set both the GA's parameter and the value in the
// parameter list (kind of stupid to maintain two copies of the same data, but
// oh well).  The call to set on params is redundant for the times when this
// method is called *after* the parameter list has been updated, but it is
// necessary when this method is called directly by the user.
int
GAGeneticAlgorithm::setptr(const char* name, const void* value){
  int status=1;

  params.set(name, value);	// redundant for some cases, but not others 

  if(strcmp(name, gaNnBestGenomes) == 0 ||
     strcmp(name, gaSNnBestGenomes) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    nBestGenomes(*((int*)value));
    status = 0;
  }
  else if(strcmp(name, gaNpopulationSize) == 0 ||
	  strcmp(name, gaSNpopulationSize) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    populationSize(*((int*)value));
    status = 0;
  }
  else if(strcmp(name, gaNminimaxi) == 0 ||
     strcmp(name, gaSNminimaxi) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    minimaxi(*((int*)value));
    status = 0;
  }
  else if(strcmp(name, gaNnGenerations) == 0 ||
     strcmp(name, gaSNnGenerations) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    nGenerations(*((int*)value));
    status = 0;
  }
  else if(strcmp(name, gaNpConvergence) == 0 || 
	  strcmp(name, gaSNpConvergence) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
    pConvergence(*((float*)value));
    status = 0;
  }
  else if(strcmp(name, gaNnConvergence) == 0 || 
	  strcmp(name, gaSNnConvergence) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    nConvergence(*((int*)value));
    status = 0;
  }
  else if(strcmp(name, gaNpCrossover) == 0 ||
	  strcmp(name, gaSNpCrossover) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
    pCrossover(*((float*)value));
    status = 0;
  }
  else if(strcmp(name, gaNpMutation) == 0 ||
	  strcmp(name, gaSNpMutation) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((float*)value) << "'\n";
#endif
    pMutation(*((float*)value));
    status = 0;
  }
  else if(strcmp(name,gaNscoreFrequency) == 0 ||
	  strcmp(name,gaSNscoreFrequency) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    stats.scoreFrequency(*((int*)value));
    status = 0;
  }
  else if(strcmp(name,gaNflushFrequency) == 0 ||
	  strcmp(name,gaSNflushFrequency) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    stats.flushFrequency(*((int*)value));
    status = 0;
  }
  else if(strcmp(name,gaNrecordDiversity) == 0 ||
	  strcmp(name,gaSNrecordDiversity) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    stats.recordDiversity(*((int*)value) ? gaTrue : gaFalse);
    status = 0;
  }
  else if(strcmp(name,gaNselectScores) == 0 ||
	  strcmp(name,gaSNselectScores)==0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << *((int*)value) << "'\n";
#endif
    stats.selectScores(*((int*)value));
    status = 0;
  }
  else if(strcmp(name,gaNscoreFilename) == 0 ||
	  strcmp(name,gaSNscoreFilename) == 0){
#ifdef GA_DEBUG
    cerr << "GAGeneticAlgorithm::setptr\n  setting '" << name << "' to '" << ((char*)value) << "'\n";
#endif
    char tmpname[64];
    memcpy(tmpname, value, strlen((char*)value)+1);
    stats.scoreFilename(tmpname);
    status = 0;
  }
  return status;
}

// This is a pretty ugly little hack to make doubles/floats work transparently.
int
GAGeneticAlgorithm::set(const char* name, double v) {
  int status=1;
  for(int i=0; i<params.size(); i++){
    if(strcmp(name, params[i].fullname()) == 0 ||
       strcmp(name, params[i].shrtname()) == 0){
      if(params[i].type() == GAParameter::FLOAT){
	float fval = (float)v;
	status = setptr(name, (void*)&fval);
      }
      else
	status = setptr(name, (void*)&v);
    }
  }
  return status;
}

int
GAGeneticAlgorithm::get(const char* name, void* value) const {
  int status=1;

  if(strcmp(name, gaNseed) == 0 ||
     strcmp(name, gaSNseed) == 0){
    *((int*)value) = d_seed;
    status = 0;
  }
  else if(strcmp(name, gaNnBestGenomes) == 0 ||
     strcmp(name, gaSNnBestGenomes) == 0){
    *((int*)value) = stats.nBestGenomes();
    status = 0;
  }
  else if(strcmp(name, gaNpopulationSize) == 0 ||
	  strcmp(name, gaSNpopulationSize) == 0){
    *((int*)value) = pop->size();
    status = 0;
  }
  else if(strcmp(name, gaNminimaxi) == 0 ||
     strcmp(name, gaSNminimaxi) == 0){
    *((int*)value) = minmax;
    status = 0;
  }
  else if(strcmp(name, gaNnGenerations) == 0 ||
     strcmp(name, gaSNnGenerations) == 0){
    *((int*)value) = ngen;
    status = 0;
  }
  else if(strcmp(name, gaNpConvergence) == 0 ||
	  strcmp(name, gaSNpConvergence) == 0){
    *((float*)value) = pconv;
    status = 0;
  }
  else if(strcmp(name, gaNnConvergence) == 0 ||
	  strcmp(name, gaSNnConvergence) == 0){
    *((int*)value) = nconv;
    status = 0;
  }
  else if(strcmp(name, gaNpCrossover) == 0 ||
	  strcmp(name, gaSNpCrossover) == 0){
    *((float*)value) = pcross;
    status = 0;
  }
  else if(strcmp(name, gaNpMutation) == 0 ||
	  strcmp(name, gaSNpMutation) == 0){
    *((float*)value) = pmut;
    status = 0;
  }
  else if(strcmp(name,gaNscoreFrequency) == 0 ||
	  strcmp(name,gaSNscoreFrequency) == 0){
    *((int*)value) = stats.scoreFrequency();
    status = 0;
  }
  else if(strcmp(name,gaNflushFrequency) == 0 ||
	  strcmp(name,gaSNflushFrequency) == 0){
    *((int*)value) = stats.flushFrequency();
    status = 0;
  }
  else if(strcmp(name,gaNrecordDiversity) == 0 ||
	  strcmp(name,gaSNrecordDiversity) == 0){
    *((int*)value) = stats.recordDiversity();
    status = 0;
  }
  else if(strcmp(name,gaNselectScores) == 0 ||
	  strcmp(name,gaSNselectScores)==0){
    *((int*)value) = stats.selectScores();
    status = 0;
  }
  else if(strcmp(name,gaNscoreFilename) == 0 ||
	  strcmp(name,gaSNscoreFilename) == 0){
    *((const char**)value) = stats.scoreFilename();
    status = 0;
  }
  return status;
}






void 
GAGeneticAlgorithm::objectiveFunction(GAGenome::Evaluator f){
  for(int i=0; i<pop->size(); i++)
    pop->individual(i).evaluator(f);
}

void
GAGeneticAlgorithm::objectiveData(const GAEvalData& v){
  for(int i=0; i<pop->size(); i++)
    pop->individual(i).evalData(v);
}

const GAPopulation&
GAGeneticAlgorithm::population(const GAPopulation& p) {
  if(p.size() < 1) {
    GAErr(GA_LOC, className(), "population", gaErrNoIndividuals);
    return *pop;
  }
  
  pop->copy(p);
  pop->geneticAlgorithm(*this);

  return *pop;
}

int
GAGeneticAlgorithm::populationSize(unsigned int value){
  unsigned int ps = value;
  params.set(gaNpopulationSize, value);
  return pop->size(ps);
}

int 
GAGeneticAlgorithm::minimaxi(int m) { 
  if(m == MINIMIZE)
    pop->order(GAPopulation::LOW_IS_BEST);
  else
    pop->order(GAPopulation::HIGH_IS_BEST);
  params.set(gaNminimaxi, m);
  minmax = (m == MINIMIZE ? MINIMIZE : MAXIMIZE);
  return minmax;
}