/**
* Calls the goal test of the problem and - if the goal test is effectively
* a {@link SolutionChecker} - additionally checks, whether the solution is
* acceptable. Solution checkers can be used to analyze several or all
* solutions with only one search run.
*/
public static bool isGoalState(Problem p, Node n)
{
bool isGoal = false;
GoalTest gt = p.getGoalTest();
if (gt.isGoalState(n.State))
{
if (gt is SolutionChecker)
{
isGoal = ((SolutionChecker)gt).isAcceptableSolution(
getSequenceOfActions(n), n.State);
}
else
{
isGoal = true;
}
}
return(isGoal);
}
public static bool isGoalState(Problem p, Node n)
{
bool isGoal = false;
GoalTest gt = p.getGoalTest();
if (gt.isGoalState(n.getState()))
{
if (gt is SolutionChecker)
{
isGoal = ((SolutionChecker)gt).isAcceptableSolution(
SearchUtils.actionsFromNodes(n.getPathFromRoot()), n
.getState());
}
else
{
isGoal = true;
}
}
return(isGoal);
}
// function GENETIC-ALGORITHM(population, FITNESS-FN) returns an individual
// inputs: population, a set of individuals
// FITNESS-FN, a function that measures the fitness of an individual
public String geneticAlgorithm(Set<String> population,
FitnessFunction fitnessFn, GoalTest goalTest)
{
String bestIndividual = null;
validatePopulation(population);
clearInstrumentation();
setPopulationSize(population.Count);
// repeat
int cnt = 0;
do
{
bestIndividual = ga(population, fitnessFn);
cnt++;
// until some individual is fit enough, or enough time has elapsed
} while (!goalTest.isGoalState(bestIndividual));
setIterations(cnt);
// return the best individual in population, according to FITNESS-FN
return bestIndividual;
}
// function GENETIC-ALGORITHM(population, FITNESS-FN) returns an individual
// inputs: population, a set of individuals
// FITNESS-FN, a function that measures the fitness of an individual
public String geneticAlgorithm(Set <String> population,
FitnessFunction fitnessFn, GoalTest goalTest)
{
String bestIndividual = null;
validatePopulation(population);
clearInstrumentation();
setPopulationSize(population.Count);
// repeat
int cnt = 0;
do
{
bestIndividual = ga(population, fitnessFn);
cnt++;
// until some individual is fit enough, or enough time has elapsed
} while (!goalTest.isGoalState(bestIndividual));
setIterations(cnt);
// return the best individual in population, according to FITNESS-FN
return(bestIndividual);
}
/**
* Returns <code>true</code> if the given state is a goal state.
*
* @param state
* an object representing a state
*
* @return <code>true</code> if the given state is a goal state.
*/
public bool isGoalState(Object state)
{
return(goalTest.isGoalState(state));
}