// Function for starting the actual evolutionary progress.
public virtual IEnumerator Evolve()
{
// Start by randomly generating the initial population.
for (int i = 0; i < populationSize; i++)
{
m_population.Add(RandomGenome());
}
// Randomly generate the first best genome.
bestGenome = RandomGenome();
// Set the starting fitness of best to be the same as default fitness of
// population genomes.
bestGenome.Fitness = 100;
// Run algorithm for the given amount of generations.
for (int i = 0; i < generations; i++)
{
feedbackText.SetText("Generation " + i + " out of " + generations + "...");
// Start simulation and wait until done.
simulationDone = false;
StartCoroutine(Simulate(m_population));
yield return(new WaitUntil(() => simulationDone));
// After simulation, start evaluation.
Evaluate();
// Add genomes to childpop until it's the same size as previous.
while (m_childPop.Count < m_population.Count)
{
Genome parent0, parent1;
// Select genomes given the results of simulation.
//RouletteSelect(out parent0, out parent1);
parent0 = TournamentSelect(m_population);
parent1 = TournamentSelect(m_population);
// Combine parents to retrieve two children.
Genome child0, child1;
Combine(out child0, out child1, parent0, parent1);
// Finally, randomly mutate children and then add them to population.
Mutate(child0.RootNode);
Mutate(child1.RootNode);
m_childPop.Add(child0);
m_childPop.Add(child1);
}
// Set the previous population to the current childPop.
m_population = m_childPop;
// Reset childpop for next generaion.
m_childPop = new List <Genome>();
}
// Save the final best tree.
FileSaver.GetInstance().SaveTree(bestGenome.RootNode, "singleEvolved");
feedbackText.SetText("Single evolution complete!");
buttonCanvas.SetActive(true);
yield return(null);
}
// Load both trees and assign them to the match simulator.
private IEnumerator StartBattle()
{
singleBT = FileSaver.GetInstance().LoadTree(singleFilename);
multiBT = FileSaver.GetInstance().LoadTree(multiFilename);
// Reset scores
m_singleWon = m_multiWon = 0;
m_singleDamage = m_multiDamage = 0;
m_singleHealth = m_multiHealth = 0;
if (singleBT == null || multiBT == null)
{
Debug.LogError("Couldn't find requested behaviourtrees.");
m_feedbackText.SetText("There doesn't exist a file for each algorithm. Please evolve using one of each before battling.");
yield return(null);
}
originalMatchLength = m_simulator.matchTime;
m_simulator.matchTime = matchLength;
// Disable button canvas before starting simulation
m_buttonCanvas.SetActive(false);
for (int i = 0; i < simulationMatches; i++)
{
m_feedbackText.SetText("Battle " + i);
// Start match and set timescale
battleOn = true;
m_simulator.liveSimulationScale = battleTimeScale;
// Start match and wait until over.
m_simulator.SetAgentBTs(singleBT, multiBT);
m_simulator.StartMatch();
yield return(new WaitUntil(() => !battleOn));
}
ScreenCapture.CaptureScreenshot("Resultat.png");
m_feedbackText.SetText("Single won: " + m_singleWon + ", Multi won: " + m_multiWon
+ "\n" + "Single damage: " + m_singleDamage + ", Multi damage: " + m_multiDamage
+ "\n" + "Single health: " + m_singleHealth + ", Multi health: " + m_multiHealth);
m_buttonCanvas.SetActive(true);
// Reset simulation scale and matchtime before yielding
m_simulator.liveSimulationScale = m_simulator.simulationTimeScale;
m_simulator.matchTime = originalMatchLength;
yield return(null);
}