/*-------------------------------------------------------------------------------------------------------------*/
/** <summary> Called to start running the algorithm in the scene. Implementation is
* specific to the child type. </summary> */
public override void Initiate_algorithm()
{
genome = new GAGenome();
//create a new flock
flock = base.Create_flock(Prefab.FLOCK_PREFAB, new HCVectorSet(genome), NUM_BOIDS);
//notify all listeners
Notify ();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary> Called to start running the algorithm in the scene. Implementation is
* specific to the child type. </summary> */
override public void Initiate_algorithm()
{
genome = new GAGenome();
//create a new flock
flock = base.Create_flock(Prefab.FLOCK_PREFAB, new HCVectorSet(genome), NUM_BOIDS);
//notify all listeners
Notify();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Creates a new flock, instantiating it, populating it, changes the camera target, and assigns a <see cref="VectorSet"/>.
* Most of this is done outside the class. </summary>
* <param name="genome"> The <see cref="GAGenome"/> for the new flock </param> */
private void Start_new_flock(GAGenome genome)
{
//create a new flock. base takes care of everything
flock = base.Create_flock(Prefab.GA_FLOCK_PREFAB, new GAVectorSet(genome), NUM_BOIDS);
//increment membersCounter
membersCounter++;
//notify all listeners that there's a new flock and information has been updated
Notify();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Sets up the scene to begin running the algorithm. </summary> */
override public void Initiate_algorithm()
{
//initialize GAGenome to find out the length of binary strings
GAGenome.Initialize();
//set up the arrays
currentGeneration = new GAGenome[MEMBERS];
//randomly generate first generation
for (int i = 0; i < MEMBERS; i++)
{
currentGeneration[i] = new GAGenome(); //create a random genome
}
//begin the update loop!
StartCoroutine(Time_flock());
}
/** <summary>
* Order the population members by their fitness level. Uses bubble sort. </summary> */
private void OrderByFitness()
{
GAGenome temp = null;
//order the array
for (int write = 0; write < currentGeneration.Length; write++)
{
//for each subsequent fitness
for (int sort = 0; sort < currentGeneration.Length - 1; sort++)
{
if (currentGeneration[sort].Fitness < currentGeneration[sort + 1].Fitness)
{
temp = currentGeneration[sort + 1];
currentGeneration[sort + 1] = currentGeneration[sort];
currentGeneration[sort] = temp;
}
}
}
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Creates a new generation of binary strings. This involves preserving elites,
* selecting mates, crossing over genomes, and mutating offspring. This method assumes that
* the currentGeneration's genomes are in order from greatest to least fitness score. </summary>
* <returns> A generation bred from the current generation </returns> */
private GAGenome[] Breed()
{
//array for the next generation.
GAGenome[] nextGeneration = new GAGenome[MEMBERS];
string[] mates = new string[2], offspring = new string[2];
int arrayPlace;
//carry the elites over into the new generation
for (arrayPlace = 0; arrayPlace < ELITES; arrayPlace++)
{
nextGeneration[arrayPlace] = new GAGenome(currentGeneration[arrayPlace].BinaryString);
}
//perform crossovers
//if (MEMBERS - ELITES - RANDOM_MEMBERS) / 2 is not a whole number, a random genome will also be generated later
for (int i = 0; i < ((MEMBERS - ELITES - RANDOM_MEMBERS) / 2); i++, arrayPlace += 2)
{
//make the selection. Options: Roulette, Rank,
mates = Selection(ROULETTE_SELECTION);
//cross the genomes
offspring = CrossGenomes(mates, 1);
//mutate the offspring
offspring[0] = Mutate(offspring[0]);
offspring[1] = Mutate(offspring[1]);
//save the offspring to the next generation array
nextGeneration[arrayPlace] = new GAGenome(offspring[0]);
nextGeneration[arrayPlace + 1] = new GAGenome(offspring[1]);
}
//fill the remainder with random new genomes
while (arrayPlace < MEMBERS)
{
nextGeneration[arrayPlace++] = new GAGenome();
}
//replace the currentGeneration
return(nextGeneration);
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Called by the Algorithm under observation. This retrieves the GAGenome of the flock
* currently active in the scene so that updated information can be displayed. </summary> */
override public void Update_listener()
{
genome = ((HillClimbing)subject).Get_current_genome();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Called by the Algorithm under observation. This retrieves the GAGenome of the flock
* currently active in the scene so that updated information can be displayed. </summary> */
override public void Update_listener()
{
genome = ((GeneticAlgorithm)subject).Get_current_genome();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Primary constructor. </summary>
* <param name="genome"> The <see cref="GACoefficient"/> information to use </param> */
public GAVectorSet(GAGenome genome)
{
this.genome = genome;
}
/*-------------------------------------------------------------------------------------------------------------*/
/** Primary Constructor.
* genome - the coefficient information to use */
public HCVectorSet(GAGenome genome)
{
this.genome = genome;
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Creates a new flock, instantiating it, populating it, changes the camera target, and assigns a <see cref="VectorSet"/>.
* Most of this is done outside the class. </summary>
* <param name="genome"> The <see cref="GAGenome"/> for the new flock </param> */
private void Start_new_flock(GAGenome genome)
{
//create a new flock. base takes care of everything
flock = base.Create_flock(Prefab.GA_FLOCK_PREFAB, new GAVectorSet(genome), NUM_BOIDS);
//increment membersCounter
membersCounter++;
//notify all listeners that there's a new flock and information has been updated
Notify ();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Creates a new generation of binary strings. This involves preserving elites,
* selecting mates, crossing over genomes, and mutating offspring. This method assumes that
* the currentGeneration's genomes are in order from greatest to least fitness score. </summary>
* <returns> A generation bred from the current generation </returns> */
private GAGenome[] Breed()
{
//array for the next generation.
GAGenome[] nextGeneration = new GAGenome[MEMBERS];
string[] mates = new string[2], offspring = new string[2];
int arrayPlace;
//carry the elites over into the new generation
for (arrayPlace = 0; arrayPlace < ELITES; arrayPlace++)
nextGeneration[arrayPlace] = new GAGenome(currentGeneration[arrayPlace].BinaryString);
//perform crossovers
//if (MEMBERS - ELITES - RANDOM_MEMBERS) / 2 is not a whole number, a random genome will also be generated later
for (int i = 0; i < ((MEMBERS - ELITES - RANDOM_MEMBERS) / 2); i++, arrayPlace += 2) {
//make the selection. Options: Roulette, Rank,
mates = Selection(ROULETTE_SELECTION);
//cross the genomes
offspring = CrossGenomes(mates, 1);
//mutate the offspring
offspring[0] = Mutate (offspring[0]);
offspring[1] = Mutate (offspring[1]);
//save the offspring to the next generation array
nextGeneration[arrayPlace] = new GAGenome(offspring[0]);
nextGeneration[arrayPlace + 1] = new GAGenome(offspring[1]);
}
//fill the remainder with random new genomes
while (arrayPlace < MEMBERS)
nextGeneration[arrayPlace++] = new GAGenome();
//replace the currentGeneration
return nextGeneration;
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Sets up the scene to begin running the algorithm. </summary> */
public override void Initiate_algorithm()
{
//initialize GAGenome to find out the length of binary strings
GAGenome.Initialize();
//set up the arrays
currentGeneration = new GAGenome[MEMBERS];
//randomly generate first generation
for (int i = 0; i < MEMBERS; i++) {
currentGeneration[i] = new GAGenome(); //create a random genome
}
//begin the update loop!
StartCoroutine(Time_flock());
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Called by the Algorithm under observation. This retrieves the GAGenome of the flock
* currently active in the scene so that updated information can be displayed. </summary> */
public override void Update_listener()
{
genome = ((GeneticAlgorithm)subject).Get_current_genome ();
}
/*-------------------------------------------------------------------------------------------------------------*/
/** <summary>
* Called by the Algorithm under observation. This retrieves the GAGenome of the flock
* currently active in the scene so that updated information can be displayed. </summary> */
public override void Update_listener()
{
genome = ((HillClimbing)subject).Get_current_genome ();
}
