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);
}
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);
}
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);
}
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);
}
static boolean struggle_generation_hook(int generation, population *pop)
{
static double sum_best_fitnesses=0.0; /* Sum of best fitness score at each generation. */
double average, stddev; /* Simple stats. */
sum_best_fitnesses += ga_get_entity_from_rank(pop,0)->fitness;
/*
* Display statistics every 20th generation.
*/
if (generation%20 == 0)
{
printf("Generation = %d\n", generation);
printf("Number of evaluations = %ld\n", evaluation_count);
printf("Best fitness = %f\n", ga_get_entity_from_rank(pop,0)->fitness);
ga_fitness_mean_stddev(pop, &average, &stddev);
printf("Mean fitness = %f, with standard deviation = %f\n", average, stddev);
if (generation>0)
printf("Average best fitness for entire run = %f\n", sum_best_fitnesses/generation);
}
/*
* Stop if we have the exact solution.
*/
if (!strncmp(target_text,
(char *)ga_get_entity_from_rank(pop,0)->chromosome[0],
strlen(target_text)))
{
printf("Exact solution has been found!\n");
return FALSE;
}
/*
* Stop if the population has converged.
*/
if (!strncmp((char *)ga_get_entity_from_rank(pop,0)->chromosome[0],
(char *)ga_get_entity_from_rank(pop,pop->size-1)->chromosome[0],
strlen(target_text)))
{
printf("Solutions have converged!\n");
return FALSE;
}
return TRUE; /* TRUE indicates that evolution should continue. */
}
boolean wildfire_ga_callback(int generation, population *pop)
{
double average, stddev; /* Statistics. */
fprintf(stderr, "%f\n", ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)));
if (generation > 0)
{
ga_population_score_and_sort(pop);
ga_fitness_mean_stddev(pop, &average, &stddev);
printf( "%d: Best %d Average %f Stddev %f\n",
generation, (int) ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)),
average, stddev );
}
else
{
printf( "Best random solution has score %d\n",
(int) ga_entity_get_fitness(ga_get_entity_from_rank(pop,0)) );
}
return TRUE; /* Always TRUE, so search doesn't finish. */
}
int main(int argc, char **argv)
{
population *pop; /* Population of solutions. */
entity *solution; /* Optimised solution. */
random_seed(23091975);
pop = ga_genesis_double(
50, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
NULL, /* GAgeneration_hook generation_hook */
test_iteration_callback, /* 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 */
NULL, /* GAselect_one select_one */
NULL, /* GAselect_two select_two */
ga_mutate_double_singlepoint_drift, /* GAmutate mutate */
NULL, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_simplex_parameters(
pop, /* population *pop */
4, /* const int num_dimensions */
0.5, /* const double Initial step size. */
test_to_double, /* const GAto_double to_double */
test_from_double /* const GAfrom_double from_double */
);
/* Evaluate and sort the initial population members (i.e. select best of 50 random solutions. */
ga_population_score_and_sort(pop);
/* Use the best population member. */
solution = ga_get_entity_from_rank(pop, 0);
ga_simplex(
pop, /* population *pop */
solution, /* entity *solution */
10000 /* const int max_iterations */
);
ga_extinction(pop);
exit(EXIT_SUCCESS);
}
static boolean test_generation_callback(int generation, population *pop)
{
entity *this_entity = ga_get_entity_from_rank(pop, 0); /* The best entity. */
printf( "%d: A = %f B = %f C = %f D = %f (fitness = %f) pop_size %d\n",
generation,
((double *)this_entity->chromosome[0])[0],
((double *)this_entity->chromosome[0])[1],
((double *)this_entity->chromosome[0])[2],
((double *)this_entity->chromosome[0])[3],
ga_entity_get_fitness(this_entity),
pop->size );
return TRUE;
}
boolean pingpong_ga_callback(int generation, population *pop)
{
int i; /* Team member. */
int score[25]; /* Scores. */
int loss=0; /* Number of matches lost. */
double lossscore=0; /* Average score in lost matches. */
entity *best; /* Top ranked solution. */
best = ga_get_entity_from_rank(pop, 0);
for (i=0; i<25; i++)
{
score[i] = (((int *)best->chromosome[0])[i] - i)*4 + 2;
if (score[i] > 0)
{
loss++;
lossscore += score[i];
}
}
lossscore /= loss;
printf( "%d: %f %d %d %d %d %d %d %d %d %d\n",
generation,
best->fitness,
((int *)best->chromosome[0])[0],
((int *)best->chromosome[0])[1],
((int *)best->chromosome[0])[2],
((int *)best->chromosome[0])[3],
((int *)best->chromosome[0])[4],
((int *)best->chromosome[0])[5],
((int *)best->chromosome[0])[6],
((int *)best->chromosome[0])[7],
((int *)best->chromosome[0])[8] );
printf( " %d %d %d %d %d %d %d %d %d Ave. loss = %f wins = %d\n",
score[0], score[1], score[2],
score[3], score[4], score[5],
score[6], score[7], score[8],
lossscore, 25-loss );
printf( " orig %d current %d\n", pop->orig_size, pop->size);
return TRUE; /* If this was to return FALSE, then the GA would terminate. */
}
boolean polynomial_generation_callback(int generation, population *pop)
{
entity *entity; /* Best ranked entity. */
/*
* This is a easy method for implementing randomly selected
* scaling factor (F in original paper) for each generation, as
* suggested in:
*
* Karaboga D., Okdem, S. "A simple and global optimization algorithm
* for engineering problems: differential evolution algorithm",
* Elec. Engin. 12:53-60 (2004).
*
* Uncomment, if desired.
*/
/*
pop->de_params->weighting_factor = random_double_range(-2.0, 2.0);
*/
/*
* Write rank 1 solution every tenth generation. Note, that this is
* not neccesarily the best solution because DE doesn't require the
* population to be sorted, as genetic algorithms usually do.
*/
if ( generation%10 == 0)
{
entity = ga_get_entity_from_rank(pop, 0);
printf( "%d: A = %f B = %f C = %f D = %f (fitness = %f)\n",
generation,
((double *)entity->chromosome[0])[0],
((double *)entity->chromosome[0])[1],
((double *)entity->chromosome[0])[2],
((double *)entity->chromosome[0])[3],
ga_entity_get_fitness(entity) );
}
return TRUE;
}
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);
}
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);
}
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);
}
int main(int argc, char **argv)
{
population *pop; /* Population of solutions. */
int i=0; /* Loop variable over strategies. */
entity *entity; /* Best ranked entity. */
random_seed(23091975);
log_init(LOG_NORMAL, NULL, NULL, FALSE);
while ( strategy[i].label != NULL )
{
if ( strategy[i].weighting_factor != strategy[i].weighting_factor2 )
{
printf( "Strategy %s ; C = %f ; F = rand( %f, %f )\n",
strategy[i].label,
strategy[i].crossover_factor,
strategy[i].weighting_factor, strategy[i].weighting_factor2 );
}
else
{
printf( "Strategy %s ; C = %f ; F = %f\n",
strategy[i].label,
strategy[i].crossover_factor,
strategy[i].weighting_factor );
}
pop = ga_genesis_double(
40, /* const int population_size */
1, /* const int num_chromo */
4, /* const int len_chromo */
polynomial_generation_callback,/* GAgeneration_hook generation_hook */
NULL, /* GAiteration_hook iteration_hook */
NULL, /* GAdata_destructor data_destructor */
NULL, /* GAdata_ref_incrementor data_ref_incrementor */
polynomial_score, /* GAevaluate evaluate */
polynomial_seed, /* GAseed seed */
NULL, /* GAadapt adapt */
NULL, /* GAselect_one select_one */
NULL, /* GAselect_two select_two */
NULL, /* GAmutate mutate */
NULL, /* GAcrossover crossover */
NULL, /* GAreplace replace */
NULL /* vpointer User data */
);
ga_population_set_differentialevolution_parameters(
pop, strategy[i].strategy, strategy[i].crossover,
strategy[i].num_perturbed, strategy[i].weighting_factor, strategy[i].weighting_factor2,
strategy[i].crossover_factor
);
ga_differentialevolution(
pop, /* population *pop */
50 /* const int max_generations */
);
entity = ga_get_entity_from_rank(pop, 0);
printf( "Final: A = %f B = %f C = %f D = %f (fitness = %f)\n",
((double *)entity->chromosome[0])[0],
((double *)entity->chromosome[0])[1],
((double *)entity->chromosome[0])[2],
((double *)entity->chromosome[0])[3],
ga_entity_get_fitness(entity) );
ga_extinction(pop);
i++;
}
exit(EXIT_SUCCESS);
}
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);
}
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);
}