private IEnumerator Evaluate(GeneticEquation equation, Candidate candidate)
{
var expression = equation.expression.Copy() as Expression;
if (expression == null)
{
yield break;
}
m_CurrentlyEvaluatedCandidate = candidate;
m_CurrentlyEvaluatedExpression = expression;
foreach (var chromosome in candidate.chromosomes)
{
foreach (var variable in expression.GetVariables())
{
if (chromosome.name == variable.stringValue)
{
variable.value = chromosome.value;
}
}
}
var fitnessEvaluation = expression.Copy() as Expression;
yield return(expression.Evaluate());
var result = expression.expressionObjects.First() as Variable;
equation.solved = (bool)result.value;
if (equation.solved)
{
candidate.fitness = 1f;
equation.solvingCandidate = candidate;
}
else
{
if (fitnessEvaluation == null)
{
yield break;
}
var expressions = fitnessEvaluation.expressionObjects.OfType <Expression>().ToList();
var trueClauses = 0f;
foreach (var expr in expressions)
{
var enumerator = expr.Evaluate();
while (enumerator.MoveNext())
{
}
var evaluation = expr.expressionObjects.First() as Variable;
if (evaluation == null)
{
continue;
}
if ((bool)evaluation.value)
{
++trueClauses;
}
}
candidate.fitness = trueClauses / expressions.Count;
}
}
private IEnumerator RunSolver()
{
foreach (var parseCNF in m_ParseCNFs)
{
m_CurrentGeneticEquation = new GeneticEquation {
expression = parseCNF.expression
};
var initialGeneration = new Generation();
for (var i = 0; i < Random.Range(15, 25); ++i)
{
initialGeneration.candidates.Add(new Candidate());
}
var sortedVariables =
parseCNF.expression.GetVariables().OrderBy(variable => variable.stringValue).ToList();
foreach (var candidate in initialGeneration.candidates)
{
foreach (var variable in sortedVariables)
{
candidate.chromosomes.Add(new Chromosome
{
value = Random.Range(0, 2) == 1,
name = variable.stringValue,
});
}
}
++m_CurrentGeneticEquation.generations;
m_CurrentGeneticEquation.currentGeneration = initialGeneration;
while (!m_CurrentGeneticEquation.solved)
{
foreach (var candidate in m_CurrentGeneticEquation.currentGeneration.candidates)
{
if (m_CurrentGeneticEquation.solved)
{
continue;
}
yield return(Evaluate(m_CurrentGeneticEquation, candidate));
}
if (m_CurrentGeneticEquation.solved)
{
continue;
}
Generation newGeneration = null;
switch (m_PopulationGenerationType)
{
case PopulationGenerationType.DoubleCrossover:
newGeneration = DoubleCrossover();
break;
case PopulationGenerationType.BasicInheritance:
newGeneration = BasicInheritance();
break;
case PopulationGenerationType.SurvivalOfTheFittest:
newGeneration = SurvivalOfTheFittest();
break;
default:
break;
}
m_CurrentGeneticEquation.currentGeneration = newGeneration;
++m_CurrentGeneticEquation.generations;
yield return(null);
}
yield return(new WaitForSeconds(3f));
}
}
