/// <summary>
/// Performs a Monte Carlo optimization on the given initial state.
/// </summary>
/// <param name="initialState"></param>
/// <returns>The most optimal state encountered after performing the specified evaluations</returns>
public override TState PerformOptimization(TState initialState)
{
if (initialState == null)
{
throw new ArgumentException("Initial state cannot be null");
}
TState bestState = initialState;
TState nextState;
for (int i = 0; i < NumRandomizations && bestState > initialState; i++) // Test this
{
nextState = successorPicker.Next(bestState);
bestState = EvaluationComparer.Compare(bestState.GetEvaluation(), nextState.GetEvaluation()) > 0
? bestState
: nextState;
}
return(bestState);
}
/// <summary>
/// Performs a Monte Carlo optimization on the given initial state.
/// </summary>
/// <param name="initialState"></param>
/// <returns>The most optimal state encountered after performing the specified evaluations</returns>
public override TState Optimize(TState initialState)
{
if (initialState == null)
{
throw new ArgumentException("Initial state cannot be null");
}
TState bestState = initialState;
TState nextState;
for (int i = 0; i < NumRandomizations; i++)
{
nextState = SuccessorPicker.Next(bestState);
bestState = EvaluationComparer.Compare(bestState, nextState) > 0
? bestState
: nextState;
}
return(bestState);
}