public GARun(
int ai_recpergen,
string as_path,
int ai_poolsize,
int ai_generations,
double ad_modifyprob,
double ad_recomprob,
Selectors aen_selector,
Recombinators aen_recomb,
Randoms aen_random,
int ai_elites = 1,
int ai_ts_contestants = 2,
bool ab_adaptivemut = true,
bool ab_rog = false,
bool ab_lrog = true
)
{
ii_recpergen = ai_recpergen;
ii_path = as_path;
ii_poolsize = ai_poolsize;
ii_generations = ai_generations;
id_modifyprob = ad_modifyprob;
id_recomprob = ad_recomprob;
ien_selector = aen_selector;
ien_recomb = aen_recomb;
ien_random = aen_random;
ii_elites = ai_elites;
ii_ts_contestants = ai_ts_contestants;
ib_adaptivemut = ab_adaptivemut;
ib_rog = ab_rog;
ib_lrog = ab_lrog;
id = id + 1;
myid = id;
}
public GARun(
int ai_recpergen,
string as_path,
int ai_poolsize,
int ai_generations,
double ad_modifyprob,
double ad_recomprob,
Selectors aen_selector,
Recombinators aen_recomb,
Randoms aen_random,
int ai_elites = 1,
int ai_ts_contestants = 2,
bool ab_adaptivemut = true,
bool ab_rog = false,
bool ab_lrog = true
)
{
ii_recpergen = ai_recpergen;
ii_path = as_path;
ii_poolsize = ai_poolsize;
ii_generations = ai_generations;
id_modifyprob = ad_modifyprob;
id_recomprob = ad_recomprob;
ien_selector = aen_selector;
ien_recomb = aen_recomb;
ien_random = aen_random;
ii_elites = ai_elites;
ii_ts_contestants = ai_ts_contestants;
ib_adaptivemut = ab_adaptivemut;
ib_rog = ab_rog;
ib_lrog = ab_lrog;
id = id + 1;
myid = id;
}
/// <summary>
/// Pass the population to the Recombination operator of choice to create children for a new generation.
/// </summary>
/// <param name="ao_pop"></param>
/// <param name="aen_recomtype"></param>
public static void Recombination(Chromosome <T>[] ao_pop, Recombinators aen_recomtype)
{
Recombination <T> lo_recom;
if (aen_recomtype == Recombinators.OnePointCrossoverPMX)
{
lo_recom = new OnePointCrossoverPMX <T>();
}
else
{
lo_recom = new TwoPointCrossoverPMX <T>();
}
ao_pop = lo_recom.GenerateChildren(ao_pop);
}
public void init
(
GARun ao_run,
ref Wrapper ao_wrapper
)
{
io_wrapper = ao_wrapper;
io_run = ao_run;
ii_recpergen = ao_run.ii_recpergen;
ii_path = ao_run.ii_path;
ii_poolsize = ao_run.ii_poolsize;
ii_generations = ao_run.ii_generations;
id_modifyprob = ao_run.id_modifyprob;
id_recomprob = ao_run.id_recomprob;
ien_selector = ao_run.ien_selector;
ien_recomb = ao_run.ien_recomb;
ien_random = ao_run.ien_random;
ii_elites = ao_run.ii_elites;
ii_ts_contestants = ao_run.ii_ts_contestants;
ib_adaptivemut = ao_run.ib_adaptivemut;
ib_rog = ao_run.ib_rog;
ib_lrog = ao_run.ib_lrog;
ida_starttime = new DateTime();
}
public void init(
GARun ao_run,
ref Wrapper ao_wrapper
)
{
io_wrapper = ao_wrapper;
io_run = ao_run;
ii_recpergen = ao_run.ii_recpergen;
ii_path = ao_run.ii_path;
ii_poolsize = ao_run.ii_poolsize;
ii_generations = ao_run.ii_generations;
id_modifyprob = ao_run.id_modifyprob;
id_recomprob = ao_run.id_recomprob;
ien_selector = ao_run.ien_selector;
ien_recomb = ao_run.ien_recomb;
ien_random = ao_run.ien_random;
ii_elites = ao_run.ii_elites;
ii_ts_contestants = ao_run.ii_ts_contestants;
ib_adaptivemut = ao_run.ib_adaptivemut;
ib_rog = ao_run.ib_rog;
ib_lrog = ao_run.ib_lrog;
ida_starttime = new DateTime();
}
/// <summary>
/// Starts the Genetic Algorithm.
/// </summary>
/// <param name="ao_data">List of IData to be converted into Genes and stored in Chromosome.</param>
/// <param name="ai_poolsize">Size of population.</param>
/// <param name="ai_generations">Number of generations to iterate algorithm.</param>
/// <param name="ad_modifyprob">Probability of modification/mutation.</param>
/// <param name="ad_recomprob">Probability of recombination/crossover.</param>
/// <param name="aen_selector">Selector Operator.</param>
/// <param name="aen_recomb">Recombination Operator.</param>
/// <param name="aen_random">Random basic vs adv</param>
/// <param name="ai_elites">Number of Elites.</param>
/// <param name="ai_ts_contestants">Number of Contestants for Tournament Selector.</param>
/// <param name="ab_adaptivemut">Adaptive Mutation Enabler.</param>
/// <param name="ab_rog">ROG Enabler.</param>
/// <param name="ab_lrog">LROG Enabler.</param>
public void Execute
(
List <T> ao_data,
int ai_poolsize,
int ai_generations,
double ad_modifyprob,
double ad_recomprob,
Selectors aen_selector,
Recombinators aen_recomb,
Randoms aen_random,
int ai_elites = 1,
int ai_ts_contestants = 2,
bool ab_adaptivemut = true,
bool ab_rog = false,
bool ab_lrog = true
)
{
//Initialize global variables.
Globals <T> .DATA = DataEncoder <T> .EncodeListFromData(ao_data);
Globals <T> .GENERATIONS = ai_generations;
Globals <T> .POOLSIZE = ai_poolsize;
Globals <T> .MODIFYPROB = ad_modifyprob;
Globals <T> .MODIFYBONUS = 0;
Globals <T> .RECOMPROB = ad_recomprob;
Globals <T> .ELITENUM = ai_elites;
Globals <T> .ADAPTMUT = ab_adaptivemut;
Globals <T> .ROG = ab_rog;
Globals <T> .LROG = ab_lrog;
if (aen_random == Randoms.Basic)
{
Globals <T> .RAND = new BasicRandom();
}
else
{
Globals <T> .RAND = new AdvRandom();
}
//Initialize local variables.
NotableChromosomes <T> lo_noteablechroms = new NotableChromosomes <T>();
Chromosome <T>[] lo_pop = new Chromosome <T> [Globals <T> .POOLSIZE];
double ld_fitness = 0;
double ld_popbestfitness = 0;
double ld_inifitness = 0;
int li_generation = 0;
//Start LROG thread if it's active.
//if (Globals<T>.LROG && io_thread == null)
//{
// Globals<T>.CPQ = new ConcurrentPriorityQueue<Chromosome<T>>(100);
// LessROG<T> lo_seeder = new LessROG<T>(ref Globals<T>.CPQ);
// io_thread = new Thread(new ThreadStart(lo_seeder.Run));
// io_thread.Start();
//}
if (Globals <T> .LROG && Globals <T> .SROGTHREAD == null)
{
Globals <T> .CPQ = new ConcurrentPriorityQueue <Chromosome <T> >(100);
LessROG <T> lo_seeder = new LessROG <T>(ref Globals <T> .CPQ);
Globals <T> .SROGTHREAD = new Thread(new ThreadStart(lo_seeder.Run));
Globals <T> .SROGTHREAD.Start();
}
//Initialize population.
ExecutionFunctions <T> .Initialize(ref ld_inifitness, ref ld_popbestfitness, lo_pop, lo_noteablechroms);
Chromosome <T>[] lo_newpop = new Chromosome <T> [Globals <T> .POOLSIZE];
//Iterate Genetic Algorithm.
while (ContinueGA(ref li_generation))
{
ExecutionFunctions <T> .EvaluateElite(lo_pop);
ExecutionFunctions <T> .Select(lo_pop, lo_newpop, aen_selector, ai_ts_contestants);
ExecutionFunctions <T> .Recombination(lo_newpop, aen_recomb);
ExecutionFunctions <T> .Modification(lo_newpop);
ExecutionFunctions <T> .EvaluateFitness(ref ld_fitness, ref ld_popbestfitness, lo_newpop, lo_noteablechroms);
lo_pop = lo_newpop;
//Send statistics to UI.
OnChanged(new APIEventArgs("", false, ld_fitness, ld_popbestfitness, lo_noteablechroms.GetFinalBest().fitness, lo_noteablechroms.GetFinalBest().ToString()));
}
//Send final statistics to UI.
OnChanged(new APIEventArgs("Initial avg fitness: ", false, ld_inifitness, ld_popbestfitness, lo_noteablechroms.GetFinalBest().fitness, lo_noteablechroms.GetFinalBest().ToString()));
OnChanged(new APIEventArgs("Final avg fitness: ", false, ld_fitness, ld_popbestfitness, lo_noteablechroms.GetFinalBest().fitness, lo_noteablechroms.GetFinalBest().ToString()));
OnChanged(new APIEventArgs("Initial best fitness: ", false, ld_fitness, ld_popbestfitness, lo_noteablechroms.GetFinalBest().fitness, lo_noteablechroms.GetFinalBest().ToString()));
OnChanged(new APIEventArgs("Overall best fitness: ", false, ld_fitness, ld_popbestfitness, lo_noteablechroms.GetFinalBest().fitness, lo_noteablechroms.GetFinalBest().ToString(), true));
}