public static Result GreedMutate(Result item)
{
Result result = new Result()
{
SequenceIndexes = item.SequenceIndexes.ToArray()
};
if (StaticRandom.Rand() % 2 == 0)
{
return(MutationLogic.Mutate(item));
}
var nucleotidIndexes = EvaluationLogic.GetWeakConnectedNucleotidIndexes(item).OrderByDescending(n => n.Item2).ToArray();
if (nucleotidIndexes.Length >= 10)
{
int firstIndex = StaticRandom.Rand(10);
int secondIndex = StaticRandom.Rand(Global.Nucleotids.Count);
int tmp = result.SequenceIndexes[firstIndex];
result.SequenceIndexes[firstIndex] = result.SequenceIndexes[secondIndex];
result.SequenceIndexes[secondIndex] = tmp;
}
else
{
return(MutationLogic.Mutate(item));
}
return(result);
}
public static Result Cross(Result item1, Result item2)
{
Result result = new Result()
{
SequenceIndexes = new int[item1.SequenceIndexes.Length]
};
int crossingPoint = StaticRandom.Rand((int)Math.Floor(item1.SequenceIndexes.Length * 0.1), (int)Math.Floor(item1.SequenceIndexes.Length * 0.9));
if (StaticRandom.Rand() % 2 == 0)
{
FillBeginning(ref result, item1.SequenceIndexes, crossingPoint);
FillEnding(ref result, item2.SequenceIndexes, crossingPoint);
return(result);
}
else
{
FillBeginning(ref result, item1.SequenceIndexes.Reverse().ToArray(), crossingPoint);
FillEnding(ref result, item2.SequenceIndexes.Reverse().ToArray(), crossingPoint);
result.SequenceIndexes = result.SequenceIndexes.Reverse().ToArray();
return(result);
}
}
public static Result GenerateRandomSolution()
{
int[] result = new int[Global.Nucleotids.Count];
for (int i = 0; i < result.Length; i++)
{
result[i] = i + 1;
}
return(new Result()
{
SequenceIndexes = result.OrderBy(i => StaticRandom.Rand()).ToArray()
});
}
public static Result Mutate(Result item)
{
Result result = new Result()
{
SequenceIndexes = item.SequenceIndexes.ToArray()
};
int index1 = StaticRandom.Rand(result.SequenceIndexes.Length - 1);
int index2 = StaticRandom.Rand(result.SequenceIndexes.Length - 1);
int tmp = result.SequenceIndexes[index1];
result.SequenceIndexes[index1] = result.SequenceIndexes[index2];
result.SequenceIndexes[index2] = tmp;
return(result);
}
public static Result Cross2Points(Result item1, Result item2)
{
Result result = new Result()
{
SequenceIndexes = new int[item1.SequenceIndexes.Length]
};
int crossingPoint1 = StaticRandom.Rand((int)(item1.SequenceIndexes.Length * 0.6));
int crossingPoint2 = StaticRandom.Rand(crossingPoint1, (int)(item1.SequenceIndexes.Length));
FillSequenceBetweenPoints(result, item1.SequenceIndexes, crossingPoint1, crossingPoint2);
List <int> order = GetFillOrderFromSequence(result, item2.SequenceIndexes, crossingPoint2);
FillResultFromOrder(result, crossingPoint1, crossingPoint2, order);
return(result);
}
public static Result GenerateGreedySolution()
{
IList <int> sequence = new List <int>();
IList <int> notUsed = Enumerable.Range(0, Global.Nucleotids.Count).ToList();
int starting = StaticRandom.Rand(Global.Nucleotids.Count - 1);
sequence.Add(starting);
notUsed.Remove(starting);
while (sequence.Count != Global.Nucleotids.Count)
{
var current = sequence.Last();
var best = notUsed.Select(i => new Tuple <int, int>(i, EvaluationLogic.GetSinglePartialSum(Global.Nucleotids[current], Global.Nucleotids[i]))).OrderBy(i => i.Item2).First().Item1;
sequence.Add(best);
notUsed.Remove(best);
}
return(new Result()
{
SequenceIndexes = sequence.Select(i => i + 1).ToArray()
});
}
public static void Execute(ref Result[] input, int tournamentSize, int singleTournamentSize = 4)
{
var slicedInput = input.Take(tournamentSize).ToArray();
Result[] randomOrder = slicedInput.OrderBy(s => StaticRandom.Rand()).ToArray();
Result[] results = new Result[slicedInput.Length / singleTournamentSize];
for (int i = 0; i < slicedInput.Length - 1; i += singleTournamentSize)
{
var bestResult = randomOrder[i];
for (int j = 1; j < singleTournamentSize; j++)
{
if (randomOrder[i + j].EvaluationPoints > bestResult.EvaluationPoints || (randomOrder[i + j].EvaluationPoints == bestResult.EvaluationPoints && randomOrder[i + j].TotalLength < bestResult.TotalLength) || (randomOrder[i + j].EvaluationPoints == bestResult.EvaluationPoints && EvaluationLogic.GetWeakConnectedNucleotidIndexes(bestResult).Count > EvaluationLogic.GetWeakConnectedNucleotidIndexes(randomOrder[i + j]).Count))
{
bestResult = randomOrder[i + j];
}
}
results[i / singleTournamentSize] = bestResult;
}
for (int i = 0; i < slicedInput.Length / singleTournamentSize; i++)
{
input[i] = results[i];
}
}
