public static List <Tuple <int, int> > GetWeakConnectedNucleotidIndexes(Result item) { var results = new List <Tuple <int, int> >(); for (int i = 1; i < item.SequenceIndexes.Length - 1; i++) { int distanceLeft = EvaluationLogic.GetSinglePartialSum(Global.Nucleotids[item.SequenceIndexes[i - 1] - 1], Global.Nucleotids[item.SequenceIndexes[i] - 1]); int distanceRight = EvaluationLogic.GetSinglePartialSum(Global.Nucleotids[item.SequenceIndexes[i] - 1], Global.Nucleotids[item.SequenceIndexes[i + 1] - 1]); if (distanceLeft + distanceRight >= Global.Nucleotids[0].Sequence.Length) { results.Add(new Tuple <int, int>(i, distanceLeft + distanceRight)); } } return(results); }
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]; } }