class BaseGA(T, uint PopSize, alias comp);
Abstract class to be used as the base for GAs.
const(T) evolve(uint generations);
- The population is created using the supplied `generator`
- Crossing-over is preformed to created missing population
- The population is mutated with probability `mutation_rate`
- The parents of the next generation are selected
- Statistics are recorded for the best individual
- Terminate if criteria is met, otherwise go to 2.
Evolution function works as follows.
float mutationRate(float rate);
const float mutationRate();
Set the mutation rate of the algorithm
float crossoverRate(float rate);
const float crossoverRate();
Set the crossover rate
uint statFrequency(uint freq);
const uint statFrequency();
Set the number of generations between printing statistics
uint randomSeed(uint seed);
const uint randomSeed();
Set the random seed to be used by the algorithm
double terminationValue(double termination);
const double terminationValue();
Set the value at which the evolution will terminate
const const(StatCollector!(T, comp)) statRecord();
Get a handle to the recorded statistics for this genome
T[] population;
Stores the population being evolved
void registerTerminationCallback(void delegate(uint) callback);
Register a function to call when the evolution is ended.
void registerGenerationCallback(void delegate(uint, T[]) callback);
Register a function to be called every generation
void generation();
Method invoked to create the initial population
void selection();
Method invoked to select the individuals to be parents to the next generation
void crossingOver();
Method invoked to create the next generation of the population
void mutation();
Method invoked to create variation in the population
const void showStatistics(int generation);
Prints the statistics of the last generation
const void showFinalStatistics(string cause);
Print the score of the historically best individual as well as the individual if it is printable