/* ----------------------------------------------------------------------------  ex26.C  mbwall 24mar96  Copyright (c) 1995-1996  Massachusetts Institute of Technology  The code for this example is adapted from an original implementation   by Thomas Grueninger (from Uni Stuttgart, visiting scholar at MIT) DESCRIPTION:   GAs are lousy at solving the travelling salesperson problem.  But here is anexample of how to do it anyway.  In this case we use a steady-state geneticalgorithm and give you the compile-time choice of partial match or edge recombination crossover.   This one looks really good when you draw an entire population of individualsand watch them all evolve in real time.  It becomes very obvious what thegenetic algorithm is doing and how well the speciation is working (or not).   This implementation is by no means efficient, so please do not complain about all of the silly little inefficient aspects of the implementation.  But it does get the job done.---------------------------------------------------------------------------- */#include <math.h>#include <ga/GASStateGA.h>#include <ga/GAListGenome.h>#include <ga/garandom.h>// Set this up for your favorite TSP.  The sample one is a contrived problem// with the towns laid out in a grid (so it is easy to figure out what the // shortest distance is, and there are many different paths with the same// shortest path).  File format is that used by the TSPLIB problems.  You can // grab more problems from // // // Apologies for using fixed-length arrays.  But this is an example, not // production code ;)#define MAX_TOWNS 50#define TSP_FILE "tsp_rect_20.txt"float DISTANCE[MAX_TOWNS][MAX_TOWNS];double x[MAX_TOWNS],y[MAX_TOWNS];int ntowns = 0;// You can use either edge recombination crossover or partial match crossover.// Which one you select makes a HUGE difference in the performance of the// genetic algorithm.  Only one of the two following lines should be commented.//#define XOVER PMXover       // (Partial Match Crossover)#define XOVER ERXover         // (Edge Recombination Crossover)float Objective(GAGenome&);int   Mutator(GAGenome&, float);void  Initializer(GAGenome&);float Comparator(const GAGenome&, const GAGenome&); int   ERXover(const GAGenome&, const GAGenome&, GAGenome*, GAGenome*);int   PMXover(const GAGenome&, const GAGenome&, GAGenome*, GAGenome*);void  ERXOneChild(const GAGenome&, const GAGenome&, GAGenome*);intmain(int argc, char** argv) {  cout << "Example 26\n\n";  cout << "The Travelling Salesman Problem (TSP) demo program.\n" << endl;// See if we've been given a seed to use (for testing purposes).  When you// specify a random seed, the evolution will be exactly the same each time// you use that seed number.  unsigned int seed = 0;  for(int ii=1; ii<argc; ii++) {    if(strcmp(argv[ii++],"seed") == 0) {      seed = atoi(argv[ii]);    }  }  double dump;  ifstream in(TSP_FILE);   if(!in) {    cerr << "could not read data file " << TSP_FILE << "\n";    exit(1);  }  ntowns=0;  do {    in >> dump;    in >> x[ntowns];    in >> y[ntowns];    ntowns++;  } while(!in.eof() && ntowns < MAX_TOWNS);  in.close();  if(ntowns >= MAX_TOWNS) {    cerr << "data file contains more towns than allowed for in the fixed\n";    cerr << "arrays.  Recompile the program with larger arrays or try a\n";    cerr << "smaller problem.\n";    exit(1);  }  double dx,dy;  for(int i=0;i<ntowns;i++) {    for(int j=i; j<ntowns;j++) {      dx=x[i]-x[j]; dy=y[i]-y[j];      DISTANCE[j][i]=DISTANCE[i][j]=sqrt(dx*dx+dy*dy);    }  }  GAListGenome<int> genome(Objective);  genome.initializer(::Initializer);  genome.mutator(::Mutator);  genome.comparator(::Comparator);  genome.crossover(XOVER);  GASteadyStateGA ga(genome);  ga.minimize();  ga.pReplacement(1.0);  ga.populationSize(100);  ga.nGenerations(1000);  ga.pMutation(0.1);  ga.pCrossover(1.0);  ga.selectScores(GAStatistics::AllScores);  ga.parameters(argc, argv);  cout << "initializing..."; cout.flush();  ga.initialize(seed);  cout << "evolving..."; cout.flush();  while(!ga.done()) {    ga.step();    if(ga.generation() % 10 == 0) {      cout << ga.generation() << " "; cout.flush();    }  }  genome = ga.statistics().bestIndividual();  cout << "the shortest path found is " << genome.score() << "\n";  cout << "this is the distance from the sequence\n";  cout << genome << "\n\n";  cout << ga.statistics() << "\n";  return 0;}// Here are the genome operators that we want to use for this problem.// Thanks to Jan Kees IJspeert for isolating an order-of-evaluation problem// in the previous implementation of this function.floatObjective(GAGenome& g) {  GAListGenome<int> & genome = (GAListGenome<int> &)g;  float dist = 0;  if(genome.head()) {    for(int i=0; i<ntowns; i++) {      int xx = *genome.current();      int yy = *genome.next();      dist += DISTANCE[xx][yy];    }  }  return dist;}voidInitializer(GAGenome& g) {  GAListGenome<int> &child=(GAListGenome<int> &)g;  while(child.head()) child.destroy(); // destroy any pre-existing list  int i,town;  static int visit[MAX_TOWNS];  memset(visit, 0, MAX_TOWNS*sizeof(int));  town=GARandomInt(0,ntowns-1);  visit[town]=1;  child.insert(town,GAListBASE::HEAD); // the head node   for( i=1; i<ntowns; i++) {    do {      town=GARandomInt(0,ntowns-1);    } while (visit[town]);    visit[town]=1;    child.insert(town);  }		// each subsequent node }intMutator(GAGenome& g, float pmut) {  GAListGenome<int> &child=(GAListGenome<int> &)g;  register int n, i;  if ((GARandomFloat() >= pmut) || (pmut <= 0)) return 0;  n = child.size();    if (GARandomFloat()<0.5) {    child.swap(GARandomInt(0,n-1),GARandomInt(0,n-1)); // swap only one time  }  else {    int nNodes = GARandomInt(1,((int)(n/2-1)));       // displace nNodes     child.warp(GARandomInt(0,n-1));                   // with or without    GAList<int> TmpList;                              // inversion    for(i=0;i<nNodes;i++) {      int *iptr = child.remove();      TmpList.insert(*iptr,GAListBASE::AFTER);      delete iptr;      child.next();    }    int invert;    child.warp(GARandomInt(0,n-nNodes));    invert = (GARandomFloat()<0.5) ? 0 : 1;    if (invert) TmpList.head(); else TmpList.tail();    for(i=0;i<nNodes;i++) {      int *iptr = TmpList.remove();      child.insert(*iptr,GAListBASE::AFTER);      delete iptr;      if (invert) TmpList.prev(); else TmpList.next();    }  }  child.head();		// set iterator to root node  return (1);}intERXover(const GAGenome& g1, const GAGenome& g2, GAGenome* c1, GAGenome* c2) {  int nc=0;  if(c1) { ERXOneChild(g1,g2,c1); nc+=1; }  if(c2) { ERXOneChild(g1,g2,c2); nc+=1; }  return nc;}voidERXOneChild(const GAGenome& g1, const GAGenome& g2, GAGenome* c1) {  GAListGenome<int> &mate1=(GAListGenome<int> &)g1;  GAListGenome<int> &mate2=(GAListGenome<int> &)g2;  GAListGenome<int> &sis=(GAListGenome<int> &)*c1;    int i,j,k,t1,t2,town;  static char CM[MAX_TOWNS][MAX_TOWNS],visit[MAX_TOWNS];  memset(CM, 0, MAX_TOWNS*MAX_TOWNS*sizeof(char));  memset(visit, 0, MAX_TOWNS*sizeof(char));  while (sis.head()) sis.destroy();  // create connection matrix  mate1.head();  for(j=0; j<ntowns; j++) {    t1 = *mate1.current(); t2 = *mate1.next();    CM[t1][t2]=1; CM[t2][t1]=1;  }  mate2.head();  for(j=0; j<ntowns; j++) {    t1 = *mate2.current(); t2 = *mate2.next();    CM[t1][t2]=1; CM[t2][t1]=1;  }    // select 1st town randomly  town=GARandomInt(0,ntowns-1);  visit[town]=1; memset(CM[town], 0, MAX_TOWNS*sizeof(char));  sis.insert(town); // the head node     GAList<int> PossFollowList;  GAList<int> FollowersList[5];  while (PossFollowList.head()) PossFollowList.destroy();  for(k=0; k<5; k++) {    while (FollowersList[k].head()) FollowersList[k].destroy();   }    // select the following town with the minimal no of next folling towns  int nPoss,nFollow;  for(i=1; i<ntowns; i++) {               nPoss = 0;    for(j=0; j<ntowns; j++) {          // no of poss. following towns      if (CM[j][town]) {	nPoss += 1;	PossFollowList.insert(j);}    }    // nPoss = 0;    if (nPoss == 0) {      do {town=GARandomInt(0,ntowns-1);} while (visit[town]); // no follower      visit[town]=1; memset(CM[town], 0, MAX_TOWNS*sizeof(char));      sis.insert(town);     }    else {      PossFollowList.head();      for(j=0; j<nPoss; j++) {	nFollow = 0; 	town = (*PossFollowList.current());	for(k=0; k<ntowns; k++) {	  if (CM[k][town]) nFollow++; 	}	FollowersList[nFollow].insert(town);	PossFollowList.next();      }      k=0;      while (FollowersList[k].size() == 0) k++;      FollowersList[k].warp(GARandomInt(0,FollowersList[k].size()));      town = (*FollowersList[k].current());      visit[town]=1; memset(CM[town], 0, MAX_TOWNS*sizeof(char));      sis.insert(town);     }    while (PossFollowList.head()) PossFollowList.destroy();    for(k=0; k<5; k++) {      while (FollowersList[k].head()) FollowersList[k].destroy();     }  }  sis.head();         // set iterator to head of list}intPMXover(const GAGenome& g1, const GAGenome& g2, GAGenome* c1, GAGenome* c2) {  GAListGenome<int> &mom=(GAListGenome<int> &)g1;  GAListGenome<int> &dad=(GAListGenome<int> &)g2;  int a = GARandomInt(0, mom.size());  int b = GARandomInt(0, dad.size());  int h;  if (b<a) { h=a; a=b; b=h; }  int* index;  int i,j,nc=0;  if(c1) {    GAListGenome<int> &sis=(GAListGenome<int> &)*c1;    sis.GAList<int>::copy(mom);    GAListIter<int> diter(dad);    index = diter.warp(a);    for(i=a; i<b; i++, index=diter.next()){      if(*sis.head() == *index){	sis.swap(i,0);      }      else{	for(j=1; (j<sis.size()) && (*sis.next() != *index); j++);	sis.swap(i,j);  // no op if j>size      }    }    sis.head();         // set iterator to head of list    nc += 1;  }  if(c2) {    GAListGenome<int> &sis=(GAListGenome<int> &)*c2;    sis.GAList<int>::copy(mom);    GAListIter<int> diter(dad);    index = diter.warp(a);    for(i=a; i<b; i++, index=diter.next()){      if(*sis.head() == *index){	sis.swap(i,0);      }      else{	for(j=1; (j<sis.size()) && (*sis.next() != *index); j++);	sis.swap(i,j);  // no op if j>size      }    }    sis.head();         // set iterator to head of list    nc += 1;  }  return nc;}floatComparator(const GAGenome& g1, const GAGenome& g2) {  GAListGenome<int> &a = (GAListGenome<int> &)g1;  GAListGenome<int> &b = (GAListGenome<int> &)g2;  int i,j,t1,t2;  float dist=ntowns;  static char CM1[MAX_TOWNS][MAX_TOWNS],CM2[MAX_TOWNS][MAX_TOWNS];  memset(CM1, 0, MAX_TOWNS*MAX_TOWNS*sizeof(char));  memset(CM2, 0, MAX_TOWNS*MAX_TOWNS*sizeof(char));  // create connection matrix CM1  a.head();  for(i=0; i<ntowns; i++) {    t1 = *a.current(); t2 = *a.next();    CM1[t1][t2]=1; CM1[t2][t1]=1;  }  // create connection matrix CM2  b.head();  for(i=0; i<ntowns; i++) {    t1 = *b.current(); t2 = *b.next();    CM2[t1][t2]=1; CM2[t2][t1]=1;  }  //calc distance = how many edges are different  for (i=0; i<ntowns; i++) {    for (j=i; j<ntowns; j++) {      if (CM1[i][j]&CM2[i][j]) dist--;    }  }  return (dist);}//   Here we override the _write method for the List class.  This lets us see// exactly what we want (the default _write method dumps out pointers to the// data rather than the data contents).//   This routine prints out the contents of each element of the list, // separated by a space.  It does not put a newline at the end of the list.//   Notice that you can override ANY function of a template class.  This is// called "specialization" in C++ and it lets you tailor the behaviour of a // template class to better fit the type.intGAListGenome<int>::write(ostream & os) const{  int *cur, *head;  GAListIter<int> tmpiter(*this);  if((head=tmpiter.head()) != 0) {    os << *head << " ";    for(cur=tmpiter.next(); cur && cur != head; cur=tmpiter.next())      os << *cur << " ";  }  return os.fail() ? 1 : 0;}#ifdef NO_AUTO_INST#include <ga/GAList.C>#include <ga/GAListGenome.C>#if defined(__GNUG__)template class GAList<int>;template class GAListGenome<int>;#elseGAList<int>;GAListGenome<int>;#endif#endif