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];
}
}
