コード例 #1
0
ファイル: pingpong.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  int		i;			/* Runs. */
  population	*pop=NULL;		/* Population of solutions. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  for (i=0; i<50; i++)
    {
    if (pop) ga_extinction(pop);

    random_seed(424242*i);

    pop = ga_genesis_integer(
       50,			/* const int              population_size */
       1,			/* const int              num_chromo */
       25,			/* const int              len_chromo */
NULL, /*pingpong_ga_callback,*/	/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       pingpong_score,		/* GAevaluate             evaluate */
       pingpong_seed,		/* GAseed                 seed */
       NULL,			/* GAadapt                adapt */
       ga_select_one_randomrank,	/* GAselect_one           select_one */
       ga_select_two_randomrank,	/* GAselect_two           select_two */
       pingpong_mutate,		/* GAmutate               mutate */
       pingpong_crossover,	/* GAcrossover            crossover */
       NULL,			/* GAreplace              replace */
       NULL			/* vpointer		User data */
            );

    ga_population_set_parameters(
       pop,			/* population      *pop */
       GA_SCHEME_DARWIN,	/* const ga_scheme_type     scheme */
       GA_ELITISM_PARENTS_SURVIVE,	/* const ga_elitism_type   elitism */
       0.5,			/* double  crossover */
       0.5,			/* double  mutation */
       0.0              	/* double  migration */
                              );

    ga_evolution(
       pop,			/* population              *pop */
       200			/* const int               max_generations */
              );

    pingpong_ga_callback(i, pop);
    }

  printf("The final solution found was:\n");
  beststring = ga_chromosome_integer_to_string(pop, ga_get_entity_from_rank(pop,0), beststring, &beststrlen);
  printf("%s\n", beststring);

  ga_extinction(pop);

  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #2
0
ファイル: struggle3.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  population	*pop=NULL;		/* Population structure. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  random_seed(23091975);

  pop = ga_genesis_char(
     120,				/* const int              population_size */
     1,					/* const int              num_chromo */
     (int) strlen(target_text),		/* const int              len_chromo */
     struggle_generation_hook, 		/* GAgeneration_hook      generation_hook */
     NULL,				/* GAiteration_hook       iteration_hook */
     NULL,				/* GAdata_destructor      data_destructor */
     NULL,				/* GAdata_ref_incrementor data_ref_incrementor */
     struggle_score,			/* GAevaluate             evaluate */
     ga_seed_printable_random,		/* GAseed                 seed */
     struggle_adaptation,		/* GAadapt                adapt */
     ga_select_one_sus,			/* GAselect_one		select_one */
     ga_select_two_sus,			/* GAselect_two		select_two */
     ga_mutate_printable_singlepoint_drift,	/* GAmutate	mutate */
     ga_crossover_char_allele_mixing,	/* GAcrossover		crossover */
     NULL,				/* GAreplace		replace */
     NULL				/* vpointer		User data */
            );

  ga_population_set_parameters(
     pop,				/* population		*pop */
     GA_SCHEME_LAMARCK_CHILDREN,	/* const ga_scheme_type	scheme */
     GA_ELITISM_PARENTS_DIE,		/* const ga_elitism_type	elitism */
     0.8,				/* const double		crossover */
     0.05,				/* const double		mutation */
     0.0				/* const double		migration */
                            );

  if ( ga_evolution( pop, 1000 )<1000 )
    {
    printf("The evolution was stopped because the termination criteria were met.\n" );
    }
  else
    {
    printf("The evolution was stopped because the maximum number of generations were performed.\n" );
    }

  printf( "The final solution with score %f was:\n",
          ga_get_entity_from_rank(pop,0)->fitness );
  beststring = ga_chromosome_char_to_string(pop, ga_get_entity_from_rank(pop,0), beststring, &beststrlen);
  printf("%s\n", beststring);
  printf( "Total number of fitness evaluations: %ld\n", evaluation_count );

  ga_extinction(pop);

  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #3
0
int main(int argc, char **argv)
  {
  population	*pop=NULL;		/* Population of solutions. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  random_seed(20092004);

  pop = ga_genesis_integer(
     200,			/* const int              population_size */
     1,				/* const int              num_chromo */
     100,			/* const int              len_chromo */
     NULL,			/* GAgeneration_hook      generation_hook */
     NULL,			/* GAiteration_hook       iteration_hook */
     NULL,			/* GAdata_destructor      data_destructor */
     NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
     all5s_score,		/* GAevaluate             evaluate */
     ga_seed_integer_random,	/* GAseed                 seed */
     NULL,			/* GAadapt                adapt */
     ga_select_one_sus,		/* GAselect_one           select_one */
     ga_select_two_sus,		/* GAselect_two           select_two */
     ga_mutate_integer_singlepoint_drift,	/* GAmutate               mutate */
     ga_crossover_integer_singlepoints,		/* GAcrossover            crossover */
     NULL,			/* GAreplace		replace */
     NULL			/* vpointer		User data */
          );

  ga_population_set_allele_min_integer(pop, 0);
  ga_population_set_allele_max_integer(pop, 10);

  ga_population_set_parameters(
     pop,			/* population      *pop */
     GA_SCHEME_DARWIN,		/* const ga_scheme_type  scheme */
     GA_ELITISM_PARENTS_SURVIVE,	/* const ga_elitism_type   elitism */
     0.8,			/* double		 crossover */
     0.05,			/* double		 mutation */
     0.0              		/* double		 migration */
                            );

  ga_evolution(
     pop,			/* population              *pop */
     250			/* const int               max_generations */
            );

/* Display final solution. */
  printf("The final solution was:\n");
  beststring = ga_chromosome_integer_to_string(pop, ga_get_entity_from_rank(pop,0), beststring, &beststrlen);
  printf("%s\n", beststring);
  printf("With score = %f\n", ga_get_entity_from_rank(pop,0)->fitness);

/* Free memory. */
  ga_extinction(pop);
  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #4
0
ファイル: struggle_threaded.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  int		i;			/* Loop over runs. */
  population	*pop=NULL;		/* Population of solutions. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  for (i=0; i<50; i++)
    {
    random_seed(i);

    pop = ga_genesis_char(
       120,			/* const int              population_size */
       1,			/* const int              num_chromo */
       (int) strlen(target_text),	/* const int              len_chromo */
       NULL,		 	/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       struggle_score,		/* GAevaluate             evaluate */
       ga_seed_printable_random,	/* GAseed                 seed */
       NULL,			/* GAadapt                adapt */
       ga_select_one_sus,	/* GAselect_one           select_one */
       ga_select_two_sus,	/* GAselect_two           select_two */
       ga_mutate_printable_singlepoint_drift,	/* GAmutate               mutate */
       ga_crossover_char_allele_mixing,	/* GAcrossover            crossover */
       NULL,			/* GAreplace		replace */
       NULL			/* vpointer		User data */
            );

    ga_population_set_parameters(
       pop,			/* population      *pop */
       GA_SCHEME_DARWIN,	/* const ga_scheme_type     scheme */
       GA_ELITISM_PARENTS_DIE,	/* const ga_elitism_type   elitism */
       0.9,			/* double  crossover */
       0.2,			/* double  mutation */
       0.0              	/* double  migration */
                              );

    ga_evolution_threaded(
       pop,			/* population      *pop */
       500			/* const int       max_generations */
              );

    printf( "The final solution with seed = %d was:\n", i );
    beststring = ga_chromosome_char_to_string(pop, ga_get_entity_from_rank(pop,0), beststring, &beststrlen);
    printf("%s\n", beststring);
    printf( "With score = %f\n", ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)) );

    ga_extinction(pop);
    }

  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #5
0
ファイル: test_io.c プロジェクト: dunghand/msrds
int main(int argc, char **argv)
  {
  population	*pop=NULL;		/* Population of solutions. */
  char		*filename_in=NULL;	/* Input filename. */
  char		*filename_out=NULL;	/* Output filename. */
  int		i;			/* Loop variable over command-line arguments. */
  int		generations=10;		/* Number of generations to perform. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  random_seed(42);

/*
 * Parse command-line.  Expect '-i FILENAME' for a population to read,
 * otherwise a new population will be created.
 * '-o FILENAME' is absolutely required to specify a file to write to.
 * If we don't get these, then we will write the options.
 */
  if (argc<2)
    {
    write_usage();
    exit(0);
    }
  for (i=1; i<argc; i++)
    {
    if (strcmp(argv[i], "-i")==0)
      { /* Read pop. */
      i++;
      if (i==argc) 
        {
        printf("Input filename not specified.\n");
        write_usage();
        exit(0);
        }
      filename_in = argv[i];
      printf("Input filename set to \"%s\"\n", filename_in);
      }
    else if (strcmp(argv[i], "-o")==0)
      {	/* Out pop. */
      i++;
      if (i==argc)
        {
        printf("Output filename not specified.\n");
        write_usage();
        exit(0);
        }
      filename_out = argv[i];
      printf("Output filename set to \"%s\"\n", filename_out);
      }
    else if (strcmp(argv[i], "-n")==0)
      { /* Number of generations requested. */
      i++;
      if (i==argc)
        {
        printf("Number of generations not specified.\n");
        write_usage();
        exit(0);
        }
      generations = atoi(argv[i]);
      printf("Number of generations set to %d.\n", generations);
      }
    else
      {	/* Error parsing args. */
      printf("Unable to parse command-line argument \"%s\"\n", argv[i]);
      write_usage();
      exit(0);
      }
    }

/*
 * Check that we had the required inputs.
 */
  if (filename_out == NULL)
    {
    printf("No output filename was specified.\n");
    write_usage();
    exit(0);
    }

/*
 * Read or create population.
 */
  if (filename_in == NULL)
    {
    pop = ga_genesis_char(
       40,			/* const int              population_size */
       1,			/* const int              num_chromo */
       strlen(target_text),	/* const int              len_chromo */
       NULL,		 	/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       struggle_score,			/* GAevaluate             evaluate */
       ga_seed_printable_random,	/* GAseed                 seed */
       NULL,				/* GAadapt                adapt */
       ga_select_one_roulette,		/* GAselect_one           select_one */
       ga_select_two_roulette,		/* GAselect_two           select_two */
       ga_mutate_printable_singlepoint_drift,	/* GAmutate               mutate */
       ga_crossover_char_allele_mixing,	/* GAcrossover            crossover */
       NULL,			/* GAreplace replace */
       NULL			/* vpointer	User data */
            );

    ga_population_set_parameters(
       pop,				/* population      *pop */
       GA_SCHEME_DARWIN,		/* const ga_scheme_type    scheme */
       GA_ELITISM_PARENTS_SURVIVE,	/* const ga_elitism_type   elitism */
       1.0,				/* double  crossover */
       0.1,				/* double  mutation */
       0.0				/* double  migration */
                              );
    }
  else
    {
    pop = ga_population_read(filename_in);
    pop->evaluate = struggle_score;	/* Custom functions can't be saved and
                                         * therefore "pop->evaluate" must be
					 * defined manually.  Likewise, if a
					 * custom crossover routine was used, for
					 * example, then that would also need
					 * to be manually defined here.
					 */
    }

  ga_evolution(
       pop,				/* population              *pop */
       generations			/* const int               max_generations */
              );

  printf("The final solution with seed = %d was:\n", i);
  beststring = ga_chromosome_char_to_string(pop, ga_get_entity_from_rank(pop,0), beststring, &beststrlen);
  printf("%s\n", beststring);
  printf("With score = %f\n", ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)) );

  ga_population_write(pop, filename_out);

  printf("Population has been saved as \"%s\"\n", filename_out);

  ga_extinction(pop);

  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #6
0
ファイル: struggle5_mpi.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  int		i;				/* Loop over populations. */
  population	*pop[GA_STRUGGLE_NUM_POPS];	/* Array of populations. */
  population	*slavepop;			/* Population for slave calculations. */
  char		*beststring=NULL;		/* Human readable form of best solution. */
  size_t	beststrlen=0;			/* Length of beststring. */
  int		rank;				/* MPI rank. */

  MPI_Init(&argc, &argv);
  MPI_Comm_rank(MPI_COMM_WORLD, &rank);

  printf("Process %d initialised (rank %d)\n", getpid(), rank);

  random_seed(42);

  if (rank != 0)
    { /* This is a slave process. */

/*
 * A population is created so that the callbacks are defined.  Evolution doesn't
 * occur with this population, so population_size can be zero.  In such a case,
 * no entities are ever seeded, so there is no significant overhead.
 * Strictly, several of these callbacks are not needed on the slave processes, but
 * their definition doesn't have any adverse effects.
 */
    slavepop = ga_genesis_char(
            0,					/* const int              population_size */
            1,					/* const int              num_chromo */
            (int) strlen(target_text),		/* const int              len_chromo */
            NULL,		 		/* GAgeneration_hook      generation_hook */
            NULL,				/* GAiteration_hook       iteration_hook */
            NULL,				/* GAdata_destructor      data_destructor */
            NULL,				/* GAdata_ref_incrementor data_ref_incrementor */
            struggle_score,			/* GAevaluate             evaluate */
            ga_seed_printable_random,		/* GAseed                 seed */
            NULL,				/* GAadapt                adapt */
            ga_select_one_sus,			/* GAselect_one           select_one */
            ga_select_two_sus,			/* GAselect_two           select_two */
            ga_mutate_printable_singlepoint_drift,	/* GAmutate               mutate */
            ga_crossover_char_allele_mixing,	/* GAcrossover            crossover */
            NULL,				/* GAreplace		replace */
            NULL				/* vpointer		User data */
            );

    printf("DEBUG: Attaching process %d\n", rank);
    ga_attach_mpi_slave( slavepop );		/* The slaves halt here until ga_detach_mpi_slaves(), below, is called. */
    }
  else
    {
/*
 * This is the master process.  Other than calling ga_evolution_mpi() instead
 * of ga_evolution(), there are no differences between this code and the usual
 * GAUL invocation.
 */

    for (i=0; i<GA_STRUGGLE_NUM_POPS; i++)
      {
      pop[i] = ga_genesis_char(
            80,			/* const int              population_size */
            1,			/* const int              num_chromo */
            (int) strlen(target_text),	/* const int              len_chromo */
            NULL,		 	/* GAgeneration_hook      generation_hook */
            NULL,			/* GAiteration_hook       iteration_hook */
            NULL,			/* GAdata_destructor      data_destructor */
            NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
            struggle_score,		/* GAevaluate             evaluate */
            ga_seed_printable_random,	/* GAseed                 seed */
            NULL,			/* GAadapt                adapt */
            ga_select_one_sus,	/* GAselect_one           select_one */
            ga_select_two_sus,	/* GAselect_two           select_two */
            ga_mutate_printable_singlepoint_drift,	/* GAmutate       mutate */
            ga_crossover_char_allele_mixing,	/* GAcrossover            crossover */
            NULL,			/* GAreplace		replace */
            NULL			/* vpointer		User data */
            );

      ga_population_set_parameters( pop[i], GA_SCHEME_DARWIN, GA_ELITISM_PARENTS_DIE, 0.75, 0.25, 0.001 );
      }

    ga_evolution_archipelago_mpi( GA_STRUGGLE_NUM_POPS, pop, 250 );

    for (i=0; i<GA_STRUGGLE_NUM_POPS; i++)
      {
      printf( "The best solution on island %d with score %f was:\n",
              i, ga_get_entity_from_rank(pop[i],0)->fitness );
      beststring = ga_chromosome_char_to_string(pop[i], ga_get_entity_from_rank(pop[i],0), beststring, &beststrlen);
      printf("%s\n", beststring);

      ga_extinction(pop[i]);
      }

    s_free(beststring);

    ga_detach_mpi_slaves();	/* Allow all slave processes to continue. */
    }

  MPI_Finalize();

  exit(EXIT_SUCCESS);
  }
コード例 #7
0
ファイル: struggle2.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  population	*popd=NULL;		/* Population for Darwinian evolution. */
  population	*popb=NULL;		/* Population for Baldwinian evolution. */
  population	*popl=NULL;		/* Population for Lamarckian evolution. */
  char		*beststring=NULL;	/* Human readable form of best solution. */
  size_t	beststrlen=0;		/* Length of beststring. */

  random_seed(23091975);

  popd = ga_genesis_char(
     150,			/* const int              population_size */
     1,				/* const int              num_chromo */
     (int) strlen(target_text),	/* const int              len_chromo */
     NULL,		 	/* GAgeneration_hook      generation_hook */
     NULL,			/* GAiteration_hook       iteration_hook */
     NULL,			/* GAdata_destructor      data_destructor */
     NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
     struggle_score,		/* GAevaluate             evaluate */
     ga_seed_printable_random,	/* GAseed                 seed */
     struggle_adaptation,	/* GAadapt                adapt */
     ga_select_one_sus,		/* GAselect_one           select_one */
     ga_select_two_sus,		/* GAselect_two           select_two */
     ga_mutate_printable_singlepoint_drift,	/* GAmutate    mutate */
     ga_crossover_char_allele_mixing,	/* GAcrossover         crossover */
     NULL,			/* GAreplace		replace */
     NULL			/* vpointer		User data */
            );

  ga_population_set_parameters(
     popd,			/* population   *pop */
     GA_SCHEME_DARWIN,		/* const ga_scheme_type scheme */
     GA_ELITISM_PARENTS_DIE,	/* const ga_elitism_type   elitism */
     0.9,			/* const double       crossover */
     0.1,			/* const double       mutation */
     0.0			/* const double       migration */
                            );

/*
 * Make exact copies of the populations, except modify
 * their evolutionary schemes.
 */
  popb = ga_population_clone(popd);
  ga_population_set_scheme(popb, GA_SCHEME_BALDWIN_CHILDREN);
  popl = ga_population_clone(popd);
  ga_population_set_scheme(popl, GA_SCHEME_LAMARCK_CHILDREN);

/*
 * Evolve each population in turn.
 */

  ga_evolution(
    popd,			/* population          *pop */
    600				/* const int           max_generations */
            );

  printf( "The final solution with Darwinian evolution with score %f was:\n",
          ga_get_entity_from_rank(popd,0)->fitness );
  beststring = ga_chromosome_char_to_string(popd, ga_get_entity_from_rank(popd,0), beststring, &beststrlen);
  printf("%s\n", beststring);

  ga_evolution(
    popb,			/* population          *pop */
    300				/* const int           max_generations */
            );

  printf( "The final solution with Baldwinian evolution with score %f was:\n",
          ga_get_entity_from_rank(popb,0)->fitness );
  beststring = ga_chromosome_char_to_string(popb, ga_get_entity_from_rank(popb,0), beststring, &beststrlen);
  printf("%s\n", beststring);

  ga_evolution(
    popl,			/* population          *pop */
    300				/* const int           max_generations */
            );

  printf( "The final solution with Lamarckian evolution with score %f was:\n",
          ga_get_entity_from_rank(popl,0)->fitness );
  beststring = ga_chromosome_char_to_string(popl, ga_get_entity_from_rank(popl,0), beststring, &beststrlen);
  printf("%s\n", beststring);

  /* Deallocate population structures. */
  ga_extinction(popd);
  ga_extinction(popb);
  ga_extinction(popl);

  /* Deallocate string buffer. */
  s_free(beststring);

  exit(EXIT_SUCCESS);
  }
コード例 #8
0
ファイル: test_moga.c プロジェクト: dunghand/msrds
int main(int argc, char **argv)
  {
  population		*pop;			/* Population of solutions. */

  random_seed(23091975);

/* "Best Set" Multiobjective GA. */
  printf("Using the Best Set Multiobjective GA varient.\n");

  pop = ga_genesis_double(
       100,			/* const int              population_size */
       1,			/* const int              num_chromo */
       4,			/* const int              len_chromo */
       test_generation_callback,/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       test_score,		/* GAevaluate             evaluate */
       test_seed,		/* GAseed                 seed */
       NULL,			/* GAadapt                adapt */
       ga_select_one_bestof2,	/* GAselect_one           select_one */
       ga_select_two_bestof2,	/* GAselect_two           select_two */
       ga_mutate_double_singlepoint_drift,	/* GAmutate               mutate */
       ga_crossover_double_doublepoints,	/* GAcrossover            crossover */
       NULL,			/* GAreplace              replace */
       NULL			/* vpointer	User data */
            );

  ga_population_set_parameters(
       pop,				/* population      *pop */
       GA_SCHEME_DARWIN,		/* const ga_scheme_type     scheme */
       GA_ELITISM_BEST_SET_SURVIVE,	/* const ga_elitism_type   elitism */
       0.8,				/* double  crossover */
       0.2,				/* double  mutation */
       0.0      		        /* double  migration */
                              );

  ga_population_set_fitness_dimensions(pop, 4);

  ga_evolution(
       pop,				/* population	*pop */
       200				/* const int	max_generations */
              );

  ga_extinction(pop);

/* "Pareto Set" Multiobjective GA. */
  printf("Using the Pareto Set Multiobjective GA varient.\n");

  pop = ga_genesis_double(
       100,			/* const int              population_size */
       1,			/* const int              num_chromo */
       4,			/* const int              len_chromo */
       test_generation_callback,/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       test_score,		/* GAevaluate             evaluate */
       test_seed,		/* GAseed                 seed */
       NULL,			/* GAadapt                adapt */
       ga_select_one_bestof2,	/* GAselect_one           select_one */
       ga_select_two_bestof2,	/* GAselect_two           select_two */
       ga_mutate_double_singlepoint_drift,	/* GAmutate               mutate */
       ga_crossover_double_doublepoints,	/* GAcrossover            crossover */
       NULL,			/* GAreplace              replace */
       NULL			/* vpointer	User data */
            );

  ga_population_set_parameters(
       pop,				/* population      *pop */
       GA_SCHEME_DARWIN,		/* const ga_scheme_type     scheme */
       GA_ELITISM_PARETO_SET_SURVIVE,	/* const ga_elitism_type   elitism */
       0.8,				/* double  crossover */
       0.2,				/* double  mutation */
       0.0      		        /* double  migration */
                              );

  ga_population_set_fitness_dimensions(pop, 4);

  ga_evolution(
       pop,				/* population	*pop */
       200				/* const int	max_generations */
              );

  ga_extinction(pop);

  exit(EXIT_SUCCESS);
  }
コード例 #9
0
ファイル: wildfire_forked.c プロジェクト: versionzero/gaul
int main(int argc, char **argv)
  {
  int		i;		/* Runs. */
  int		j;		/* Loop variable. */
  population	*pop=NULL;	/* Population of solutions. */
  int		map[WILDFIRE_X_DIMENSION*WILDFIRE_Y_DIMENSION];	/* Map. */
  int		count=0;	/* Number of cisterns. */

  random_seed(23091975);

  pop = ga_genesis_integer(
       100,			/* const int              population_size */
       1,			/* const int              num_chromo */
       WILDFIRE_X_DIMENSION*WILDFIRE_Y_DIMENSION,/* const int      len_chromo */
       wildfire_ga_callback,	/* GAgeneration_hook      generation_hook */
       NULL,			/* GAiteration_hook       iteration_hook */
       NULL,			/* GAdata_destructor      data_destructor */
       NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
       wildfire_score,		/* GAevaluate             evaluate */
       wildfire_seed,		/* GAseed                 seed */
       NULL,			/* GAadapt                adapt */
       ga_select_one_roulette_rebased,	/* GAselect_one           select_one */
       ga_select_two_roulette_rebased,	/* GAselect_two           select_two */
       wildfire_mutate_flip,	/* GAmutate               mutate */
       wildfire_crossover,	/* GAcrossover   crossover */
       NULL,			/* GAreplace     replace */
       NULL			/* vpointer	User data */
            );

  ga_population_set_parameters(
       pop,			/* population      *pop */
       GA_SCHEME_DARWIN,		/* const ga_scheme_type    scheme */
       GA_ELITISM_PARENTS_SURVIVE,	/* const ga_elitism_type   elitism */
       0.8,			/* double  crossover */
       0.2,			/* double  mutation */
       0.0              	/* double  migration */
                              );

  ga_evolution_forked(
       pop,		/* population              *pop */
       250		/* const int               max_generations */
              );

  printf( "Best solution, with score %d, was:\n",
          (int) ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)) );
  /* Decode chromsome, and count number of cisterns. */
  for(i=0; i<WILDFIRE_X_DIMENSION*WILDFIRE_Y_DIMENSION; i++)
    {
    map[i] = ((int *)ga_get_entity_from_rank(pop,0)->chromosome[0])[i];
    if (map[i]) count++;
    }
  printf("%d cisterns\n", count);

  for (i=0; i<WILDFIRE_Y_DIMENSION; i++)
    {
    for (j=0; j<WILDFIRE_X_DIMENSION; j++)
      {
      printf("%s ", ((int *)ga_get_entity_from_rank(pop,0)->chromosome[0])[i*WILDFIRE_X_DIMENSION+j]?"X":"-");
      }
    printf("\n");
    }

  wildfire_simulation(map, TRUE);
  printf("\n");
  wildfire_simulation(map, TRUE);
  printf("\n");
  wildfire_simulation(map, TRUE);
  printf("\n");
  wildfire_simulation(map, TRUE);
  printf("\n");

  ga_extinction(pop);

  exit(EXIT_SUCCESS);
  }
コード例 #10
0
ファイル: ga_phasing.c プロジェクト: FXIhub/hawk
void genetic_reconstruction(Image * _amp, Image * initial_support, Image * _exp_sigma,
			     Options * _opts, char * dir){

  char prev_dir[1024];
  real support_threshold = _opts->new_level;
  population *pop;			/* Population of solutions. */

  random_seed(23091975);
  stop_threshold = 10;
  stop = 0;
  support_size = -support_threshold;
  opts = _opts;
  amp = _amp;
  exp_sigma = _exp_sigma;
  
  init_log(&my_log);
  my_log.threshold = support_threshold;
  opts->cur_iteration = 0;
  opts->flog = NULL;
  if(opts->automatic){
    opts->algorithm = HIO;
  }
  
  support = imgcpy(initial_support);
  prev_support = imgcpy(initial_support);

  /* Set the initial guess */
  if(opts->image_guess){
    real_in = imgcpy(opts->image_guess);
  }else{
    real_in = imgcpy(support);
  }

  /* make sure we make the input complex */
  rephase(real_in);
  
  /* Set random phases if needed */
  if(opts->rand_phases){
    /*    set_rand_phases(real_in,img);*/
    set_rand_ints(real_in,amp);
  }

  getcwd(prev_dir,1024);
  mkdir(dir,0755);
  chdir(dir);
  write_png(support,"support.png",COLOR_JET);
  write_png(real_in,"initial_guess.png",COLOR_JET);
  write_png(initial_support,"initial_support.png",COLOR_JET);

  if(get_algorithm(opts,&my_log) == HIO){     
    real_out = basic_hio_iteration(amp, real_in, support,opts,&my_log);
  }else if(get_algorithm(opts,&my_log) == RAAR){
    real_out = basic_raar_iteration(amp,exp_sigma, real_in, support,opts,&my_log);
  }else if(get_algorithm(opts,&my_log) == HPR){
    real_out = basic_hpr_iteration(amp, real_in, support,opts,&my_log);
  }else{
    fprintf(stderr,"Error: Undefined algorithm!\n");
    exit(-1);
  }

  radius = opts->max_blur_radius;

  
  pop = ga_genesis_double(
			  3,			/* const int              population_size */
			  1,			/* const int              num_chromo */
			  TSIZE(amp)*2,	/* const int              len_chromo */
			  test_generation_callback,/* GAgeneration_hook      generation_hook */
			  NULL,			/* GAiteration_hook       iteration_hook */
			  NULL,			/* GAdata_destructor      data_destructor */
			  NULL,			/* GAdata_ref_incrementor data_ref_incrementor */
			  test_score,		/* GAevaluate             evaluate */
			  test_seed,		/* GAseed                 seed */
			  test_adaptation,	/* GAadapt                adapt */
			  ga_select_one_bestof2,	/* GAselect_one           select_one */
			  ga_select_two_bestof2,	/* GAselect_two           select_two */
			  ga_mutate_double_singlepoint_drift,	/* GAmutate               mutate */
			  ga_crossover_double_doublepoints,	/* GAcrossover            crossover */
			  NULL,			/* GAreplace              replace */
			  NULL			/* vpointer	User data */
			  );


  ga_population_set_parameters(
       pop,				/* population      *pop */
       GA_SCHEME_LAMARCK_ALL,		/* const ga_scheme_type     scheme */
       GA_ELITISM_PARENTS_SURVIVE,	/* const ga_elitism_type   elitism */
       0.8,				/* double  crossover */
       0.2,				/* double  mutation */
       0.0      		        /* double  migration */
                              );

  ga_evolution(
       pop,				/* population	*pop */
       500				/* const int	max_generations */
              );

  ga_extinction(pop);
  exit(EXIT_SUCCESS);
}
コード例 #11
0
ファイル: rlc_fitting.c プロジェクト: mingdaqiu/MathModel2012
int main(int argc, char **argv)
{
	population		*pop;			/* Population of solutions. */
	fitting_data_t	data;	/* Training data. */
	FILE * fData;

	random_seed(time(NULL));
	double params_d[4];
	double x;


	// fitting
	if(argc >= 3 && strcmp(argv[1], "fit") == 0)
	{

		pop = ga_genesis_char(
		   100,				/* const int              population_size */
		   1,				/* const int              num_chromo */
		   3,				/* const int              len_chromo */
		   fitting_generation_callback,	/* GAgeneration_hook      generation_hook */
		   NULL,				/* GAiteration_hook       iteration_hook */
		   NULL,				/* GAdata_destructor      data_destructor */
		   NULL,				/* GAdata_ref_incrementor data_ref_incrementor */
		   fitting_score,			/* GAevaluate             evaluate */
		   //fitting_seed,			/* GAseed                 seed */
		   ga_seed_char_random,
		   NULL,				/* GAadapt                adapt */
		   //ga_select_one_linearrank,	
		   ga_select_one_random,	/* GAselect_one           select_one */
		   ga_select_two_random,	/* GAselect_two           select_two */
		   ga_mutate_char_singlepoint_randomize,/* GAmutate               mutate */
		   ga_crossover_char_allele_mixing,/* GAcrossover            crossover */
		   NULL,				/* GAreplace              replace */
		   NULL				/* vpointer	User data */
				);

		ga_population_set_parameters(
		   pop,				/* population      *pop */
		   GA_SCHEME_DARWIN,		/* const ga_scheme_type     scheme */
		   GA_ELITISM_PARENTS_DIE,		/* const ga_elitism_type   elitism */
		   0.8,				/* double  crossover */
		   0.8,				/* double  mutation */
		   0.0      		        /* double  migration */
								  );

		fData = fopen("data", "r");

		debug_message("Begin Get Data\n");
		get_data(fData, &data);
		pop->data = &data;
		debug_message("Done Get Data\n");

		debug_message("Begin Evolution\n");
		ga_evolution(
			pop,				/* population	*pop */
			200				/* const int	max_generations */
		);
		debug_message("Done Evolution\n");

		ga_extinction(pop);
	}
	// plot
	else if(argc >= 5 && strcmp(argv[1], "plot")==0)
	{
		params_d[0] = E_GIVEN;			
		params_d[1] = atof(argv[2]);
		params_d[2] = atof(argv[3]);
		params_d[3] = atof(argv[4]);

		for(x = 0; x < 10; x += 0.01)
		{
			fprintf(stdout, "%lf %lf\n", x, target_function(x, params_d));	
		}
	}
	else
	{
		fprintf(stdout, "usage: %s [plot/fit] [params]\n", argv[0]);
	}
}
コード例 #12
0
ファイル: testGA.cpp プロジェクト: xushutan/aDock
int
testVdwGA(){
	//--------------------- protein -------------------------------------
	string							fileNameA ( "/mnt/2t/xushutan/project/gold_dock/1a9u_diverse500/1A9U_protein.mol2" ) ;
	Molecular						molA( fileNameA );
	molA = readMolecularFile( fileNameA ).front();
	mol = molA;

	bindingAtomVec = getBindingSiteAtoms( mol, bindingCenter, bindingDiameter );
	if(1){
		cout<<"binding site atoms:"<<bindingAtomVec.size()<<endl;
		for( size_t i=0; i<bindingAtomVec.size(); i++ ){
			bindingAtomVec[i].print();
		}
	}
	vector<Atom>			protHbAcceptorAtoms = goldPar.getHydrogenBondAcceptorAtoms( bindingAtomVec );
	vector<Atom>			protHbDonorAtoms = goldPar.getHydrogenBondDonorAtoms( bindingAtomVec );

	if(0){
		cout<<"prot acceptor:"<<protHbAcceptorAtoms.size()<<endl;
		for( size_t i=0; i<protHbAcceptorAtoms.size(); i++ ){
			protHbAcceptorAtoms[i].print();
		}
		cout<<"prot donor:"<<protHbDonorAtoms.size()<<endl;
		for( size_t i=0; i<protHbDonorAtoms.size(); i++ ){
			protHbDonorAtoms[i].print();
		}
	}

	//---------------------------- ligand -----------------------------------
	string							fileNameB = "/mnt/2t/xushutan/project/gold_dock/1a9u_diverse500/ligand_corina.mol2";
	Molecular						molB;
	molB = readMolecularFile( fileNameB ).front();

	vector<Atom>			ligHbAcceptorAtoms = goldPar.getHydrogenBondAcceptorAtoms( molB.get_atomVec() );
	vector<Atom>			ligHbDonorAtoms = goldPar.getHydrogenBondDonorAtoms( molB.get_atomVec() );

	if(0){
		cout<<"lig acceptor:"<<ligHbAcceptorAtoms.size()<<endl;
		for( size_t i=0; i<ligHbAcceptorAtoms.size(); i++ ){
			ligHbAcceptorAtoms[i].print();
		}
		cout<<"lig donor:"<<ligHbDonorAtoms.size()<<endl;
		for( size_t i=0; i<ligHbDonorAtoms.size(); i++ ){
			ligHbDonorAtoms[i].print();
		}
		cout<<"-----------"<<endl;
	}

	Coord							ligand_N25( 0.1835,    0.8981,    0.1207 );
	Coord							standardLigand_NC3(  3.576,  15.169,  28.381 );

	Coord							direction = standardLigand_NC3 - ligand_N25 ;
	double						distance = getCoordDis( standardLigand_NC3, ligand_N25 );
	ligand = translateMol( molB, direction, distance );
	ligand.print();

	rotableBondVec = getRotableBonds( molB );

	if(1){
		cout<<"rotableBondNum:"<<rotableBondVec.size()<<endl;
		for( size_t i=0; i<rotableBondVec.size(); i++ ){
			rotableBondVec[i].print();
		}
	}

	population *pop=NULL;	/* The population of solutions. */

	int 	chromoLength = 7 + rotableBondVec.size();
	cout<<"chromolength:"<<  rotableBondVec.size()<<endl;;
	random_seed( 10000 );
	pop = ga_genesis_double(
			6,												// population size
			1, 												// num_chromo: ligand position, rotation and bond rotation
			7 + rotableBondVec.size(),			// chromo length, here 7 is position 3 + rotation 3 + translate distance 1
			NULL,
			NULL,
			NULL,
			NULL,
			docking_score,
//			ga_seed_printable_random,
			initialChromo,
			NULL,
			ga_select_one_sus,        /* GAselect_one           select_one */
			ga_select_two_sus,        /* GAselect_two           select_two */
			ga_mutate_double_multipoint, /* GAmutate  mutate */
			ga_crossover_double_mean, /* GAcrossover     crossover */
			NULL,                     /* GAreplace              replace */
			NULL                      /* void *                 userdata */
			);
	ga_population_set_parameters(
			pop,                     /* population              *pop */
			GA_SCHEME_DARWIN,        /* const ga_class_type     class */
			GA_ELITISM_PARENTS_DIE,  /* const ga_elitism_type   elitism */
			0.9,                     /* double                  crossover */
			0.2,                     /* double                  mutation */
			0.0                      /* double                  migration */
	);
	ga_evolution(
			pop,                     /* population              *pop */
			int(2)                    /* const int               max_generations */
	);
	printf( "Fitness score = %f\n", ga_get_entity_from_rank(pop,0)->fitness);
	ga_extinction(pop);	/* Deallocates all memory associated with  the population and it's entities. */
	exit(EXIT_SUCCESS);

}