C++ GAPopulation::ave方法代码示例

// This is an implementation of the sigma truncation scaling method descibed in
// goldberg p 124.  If the scaled fitness is less than zero, we arbitrarily set
// it to zero (thus the truncation part of 'sigma truncation').
void 
GASigmaTruncationScaling::evaluate(const GAPopulation & p) {
  for(int i=0; i<p.size(); i++){
    double f = (double)(p.individual(i).score()) - (double)(p.ave());
    f += (double)c * (double)(p.dev());
    if(f < 0) f = 0.0;
    p.individual(i).fitness((float)f);       // might lose information here!
  }
}

// Reset the GA's statistics based on the population.  To do this right you
// should initialize the population before you pass it to this routine.  If you
// don't, the stats will be based on a non-initialized population.
void
GAStatistics::reset(const GAPopulation & pop){
  curgen = 0;
  numsel = numcro = nummut = numrep = numeval = numpeval = 0;

  memset(gen, 0, Nscrs*sizeof(int));
  memset(aveScore, 0, Nscrs*sizeof(float));
  memset(maxScore, 0, Nscrs*sizeof(float));
  memset(minScore, 0, Nscrs*sizeof(float));
  memset(devScore, 0, Nscrs*sizeof(float));
  memset(divScore, 0, Nscrs*sizeof(float));
  nscrs = 0;
  setScore(pop);
  if(Nscrs > 0) flushScores();

  memset(cscore, 0, Nconv*sizeof(float));
  nconv = 0;			// should set to -1 then call setConv
  cscore[0] = 
    ((pop.order() == GAPopulation::HIGH_IS_BEST) ? pop.max() : pop.min());
//  cscore[0] = pop.max();
//  setConvergence(maxScore[0]);

  updateBestIndividual(pop, gaTrue);
  aveCur = aveInit = pop.ave();
  maxCur = maxInit = maxever = pop.max();
  minCur = minInit = minever = pop.min();
  devCur = devInit = pop.dev();
  divCur = divInit = ((dodiv == gaTrue) ? pop.div() : (float)-1.0);

  on = pop.ave();
  offmax = pop.max();
  offmin = pop.min();
  numpeval = pop.nevals();
  for(int i=0; i<pop.size(); i++)
    numeval += pop.individual(i).nevals();
}

// Set the score info to the appropriate values.  Update the score count.
void
GAStatistics::setScore(const GAPopulation& pop){ 
  aveCur = pop.ave();
  maxCur = pop.max();
  minCur = pop.min();
  devCur = pop.dev();
  divCur = ((dodiv == gaTrue) ? pop.div() : (float)-1.0);

  if(Nscrs == 0) return;
  gen[nscrs] = curgen;
  aveScore[nscrs] = aveCur;
  maxScore[nscrs] = maxCur;
  minScore[nscrs] = minCur;
  devScore[nscrs] = devCur;
  divScore[nscrs] = divCur;
  nscrs++;
}

// Use this method to update the statistics to account for the current
// population.  This routine increments the generation counter and assumes that
// the population that gets passed is the current population.
//   If we are supposed to flush the scores, then we dump them to the specified
// file.  If no flushing frequency has been specified then we don't record.
void
GAStatistics::update(const GAPopulation & pop){
  ++curgen;			// must do this first so no divide-by-zero
  if(scoreFreq > 0 && (curgen % scoreFreq == 0)) setScore(pop);
  if(Nscrs > 0 && nscrs >= Nscrs) flushScores();
  maxever = (pop.max() > maxever) ? pop.max() : maxever;
  minever = (pop.min() < minever) ? pop.min() : minever;
  float tmpval;
  tmpval = (on*(curgen-1) + pop.ave()) / curgen;
  on = tmpval;
  tmpval = (offmax*(curgen-1) + pop.max()) / curgen;
  offmax = tmpval;
  tmpval = (offmin*(curgen-1) + pop.min()) / curgen;
  offmin = tmpval;
  setConvergence((pop.order() == GAPopulation::HIGH_IS_BEST) ?
		 pop.max() : pop.min());
  updateBestIndividual(pop);
  numpeval = pop.nevals();
}

void 
GALinearScaling::evaluate(const GAPopulation & p) {
// Here we calculate the slope and intercept using the multiplier and objective
// score ranges...

  double pmin = p.min();
  double pmax = p.max();
  double pave = p.ave();

  double delta, a, b;
  if(pave == pmax){	// no scaling - population is all the same
    a = 1.0; 
    b = 0.0;
  }
  else if(pmin > ((double)c * pave - pmax)/((double)c - 1.0)){
    delta = pmax - pave;
    a = ((double)c - 1.0) * pave / delta;
    b = pave * (pmax - (double)c * pave) / delta;
  }
  else{				// stretch to make min be 0
    delta = pave - pmin;
    a = pave / delta;
    b = -pmin * pave / delta;
  }

// and now we calculate the scaled scaled values.  Negative scores are not
// allowed with this kind of scaling, so check for negative values.  If we get
// a negative value, dump an error message then set all of the scores to 0.

  for(int i=0; i<p.size(); i++){
    double f = p.individual(i).score();
    if(f < 0.0){
      GAErr(GA_LOC, className(), "evaluate", gaErrNegFitness);
      for(int ii=0; ii<p.size(); ii++)
	p.individual(ii).fitness(0.0);
      return;
    }
    f = f * a + b;
    if(f < 0) f = 0.0;	// truncate if necessary (only due to roundoff error)
    p.individual(i).fitness((float)f);       // might lose information here!
  }
}