/// <summary> /// SIMONObjectCollection 집합에 대해서 다음 세대의 DNA를 적용시켜서 진화시킵니다. /// </summary> /// <param name="ObjectCollection">3차원의 대상 SIMON Collection 입니다.</param> /// <param name="ActionMap">3차원의 ActionMap 입니다.</param> /// <param name="nextDNA">진화시킬 다음 세대의 3차원 DNA입니다.</param> private void Evolution(SIMONCollection ObjectCollection, SIMONCollection ActionMap, List<List<List<SIMONGene>>> nextDNA) { if (ObjectCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0) return; int numberOfDNA = nextDNA.Count; for (int i = 0; i < ActionMap.Count; i++) { if (nextDNA[i].Count < 1) continue; Random rand = new Random(); List<SIMONObject> actionObjectList = (List<SIMONObject>)ActionMap.ValueOfIndex(i); for (int j = 0; j < actionObjectList.Count; j++) { for (int k = 0; k < actionObjectList[j].Actions.Count; k++) { if (ActionMap.KeyOfIndex(i).Equals(actionObjectList[j].Actions[k].ActionName)) { int geneIdx = rand.Next(0, nextDNA[i].Count); ((List<SIMONObject>)ActionMap.ValueOfIndex(i))[j].Actions[k].ActionDNA = nextDNA[i][geneIdx]; break; } } } } }
/// <summary> /// SIMONObjectCollection 집합에 대해서 다음 세대의 DNA를 적용시켜서 진화시킵니다. /// </summary> /// <param name="ObjectCollection">3차원의 대상 SIMON Collection 입니다.</param> /// <param name="ActionMap">3차원의 ActionMap 입니다.</param> /// <param name="nextDNA">진화시킬 다음 세대의 3차원 DNA입니다.</param> /// <param name="learningRate">진화의 학습률입니다.</param> private void Evolution(SIMONCollection ObjectCollection, SIMONCollection ActionMap, List<List<List<SIMONGene>>> nextDNA, double learningRate) { if (ObjectCollection.Count <= 0 || nextDNA == null || nextDNA.Count <= 0) return; int numberOfDNA = nextDNA.Count; for (int i = 0; i < ActionMap.Count; i++) { if (nextDNA[i].Count < 1) continue; int actionObjDNACnt = nextDNA[i].Count; for (int j = 0; j < actionObjDNACnt; j++) { int nextDNAGeneCnt = nextDNA[i][j].Count; for (int k = 0; k < nextDNAGeneCnt; k++) { nextDNA[i][j][k].ElementWeight *= learningRate; } } } for (int i = 0; i < ActionMap.Count; i++) { if (nextDNA[i].Count < 1) continue; Random rand = new Random(); int randIdx = rand.Next(0, numberOfDNA); List<SIMONObject> actionObjectList = (List<SIMONObject>)ActionMap.ValueOfIndex(i); for (int j = 0; j < actionObjectList.Count; j++) { for (int k = 0; k < actionObjectList[j].Actions.Count; k++) { if (ActionMap.KeyOfIndex(i).Equals(actionObjectList[j].Actions[k].ActionName)) { int geneIdx = rand.Next(0, nextDNA[i].Count); ((List<SIMONObject>)ActionMap.ValueOfIndex(i))[j].Actions[k].ActionDNA = nextDNA[i][geneIdx]; } } } } }
/// <summary> /// History로부터 읽어온 데이터를 바탕으로, 유전 선택 알고리즘을 수행합니다. 우성 : 열성이 3:1 비율이 되도록 집단에서 우성 행동들과 열성 행동의 3차원 배열값을 반환합니다. /// </summary> /// <param name="ObjectCollection">3차원으로 구성된 Selection 대상 ObjectCollection 입니다.</param> /// <param name="ActionMap">Selection 대상 ActionMap 입니다.</param> /// <param name="SimonFunctions">Selection 연산에 사용될 SimonFunction 집합입니다.</param> /// <returns>선택된 3차원 ActionDNA 배열입니다.</returns> public List<List<List<SIMONGene>>> Selection(SIMONCollection ObjectCollection, SIMONCollection ActionMap, Dictionary<string, SIMONFunction> SimonFunctions) { List<List<List<SIMONGene>>> selectedDNA = new List<List<List<SIMONGene>>>(); List<GeneValue[]> recordMap = new List<GeneValue[]>(); //ObjectCollection 내 각 객체들에 대한 Action들에 대한 Record 값들을 저장하는 Map. int ObjectCount = ObjectCollection.Count; int ActionCount = ActionMap.Count; for (int i = 0; i < ActionCount; i++) { selectedDNA.Add(new List<List<SIMONGene>>()); } #region 현재 ActionMap 구조를 이용한 RecordMap 구조화 for (int i = 0; i < ActionCount; i++) { List<SIMONObject> elementList = (List<SIMONObject>)ActionMap.ValueOfIndex(i); int actionObjectCount = elementList.Count; GeneValue[] gene = new GeneValue[actionObjectCount]; recordMap.Add(gene); for (int j = 0; j < actionObjectCount; j++) { recordMap[i][j] = new GeneValue(); SIMONObject[] otherObjectsList; if (ObjectCount > 1) otherObjectsList = new SIMONObject[ObjectCollection.Count - 1]; else otherObjectsList = null; int otherObjectCnt = 0; for (int k = 0; k < ObjectCount; k++) if ((otherObjectsList != null) && (!ObjectCollection.ValueOfIndex(k).Equals(elementList[j]))) otherObjectsList[otherObjectCnt++] = (SIMONObject)ObjectCollection.ValueOfIndex(k); for (int k = 0; k < elementList[j].Actions.Count; k++) { if (elementList[j].Actions[k].ActionName.Equals(ActionMap.KeyOfIndex(i))) { recordMap[i][j].dna = elementList[j].Actions[k].ActionDNA; double fitnessValue = (double)SimonFunctions[elementList[j].Actions[k].FitnessFunctionName].Invoke(elementList[j], otherObjectsList); //Upper Boundary와 Lower Boundary 내의 Fitness Value들을 채택. if (fitnessValue < SIMONConstants.FITNESS_MIN_VALUE) { throw new SIMONFramework.ValueUnderflowException(SIMONConstants.EXP_VALUE_UNDERFLOW); } else if (fitnessValue > SIMONConstants.FITNESS_MAX_VALUE) { throw new SIMONFramework.ValueOverflowException(SIMONConstants.EXP_VALUE_OVERFLOW); } recordMap[i][j].fitnessValue = fitnessValue; break; } } } } #endregion #region 현재 division 나누는 코드. Action 별로 4등분해서 나누기 때문에 2차원 배열임. int[][] divIndex = new int[ActionCount][]; for (int i = 0; i < ActionCount; i++) { int numberOfActionObjects = ((List<SIMONObject>)ActionMap.ValueOfIndex(i)).Count; divIndex[i] = new int[SIMONConstants.GENE_SUM_RATING]; for (int j = 0; j < SIMONConstants.GENE_SUM_RATING; j++) { int divPosition = (numberOfActionObjects * (j + 1)) / SIMONConstants.GENE_SUM_RATING; if (divPosition == 0) divPosition = -1; else divPosition--; divIndex[i][j] = divPosition; } } #endregion //QuickSort를 통해서 각 Action별로 fitness 값들을 정렬. for (int i = 0; i < recordMap.Count; i++) QuickSort(recordMap[i], 0, recordMap[i].Length - 1); List<List<GeneValue>> firstSelectGene = new List<List<GeneValue>>(); List<List<GeneValue>> lastSelectGene = new List<List<GeneValue>>(); for (int i = 0; i < recordMap.Count; i++) { firstSelectGene.Add(new List<GeneValue>()); lastSelectGene.Add(new List<GeneValue>()); List<GeneValue> recessiveGroup = new List<GeneValue>(); List<GeneValue> dominionGroup = new List<GeneValue>(); //열성집합 중 1만큼의 비율을 선택. 경계값부터 시작인덱스 까지 내려가면서 집합에 포함시킨다. for (int j = divIndex[i][(SIMONConstants.GENE_RECESSIVE_RATING - 1)]; j >= 0; j--) { recessiveGroup.Add(recordMap[i][j]); } List<GeneValue> rouletteRecessive = RouletteWheel(recessiveGroup, GeneSelectionLaw.RECESSIVE); //각 Object 들의 열성 집합 중 대표값 배열을 우성 열성 비율별로 선택 if (rouletteRecessive != null) firstSelectGene[i].AddRange(rouletteRecessive); //우성집합 중 3만큼의 비율을 선택. 경계값부터 시작인덱스까지 내려가면서 집합에 포함시킨다. for (int j = divIndex[i][SIMONConstants.GENE_DOMINION_RATING]; j >= divIndex[i][(SIMONConstants.GENE_RECESSIVE_RATING - 1)] + 1; j--) { dominionGroup.Add(recordMap[i][j]); } List<GeneValue> rouletteDominion = RouletteWheel(dominionGroup, GeneSelectionLaw.DOMINION); //각 Object 들의 우성 집합 중 대표값 배열을 우성 열성 비율별로 선택 if (rouletteDominion != null) firstSelectGene[i].AddRange(rouletteDominion); lastSelectGene[i].AddRange(RouletteWheel(firstSelectGene[i], GeneSelectionLaw.DOMINION)); } for (int i = 0; i < ActionCount; i++) { int selectedCount = SIMONConstants.GENE_REAL_SELECT_NUM; int[] selectedIdxList = new int[lastSelectGene[i].Count]; int selectedIdxListIndex = 0; //만약 실제 유전자 숫자가 Default로 정한 유전자 추출 갯수보다 작으면 실제 유전자 갯수 만큼을 선택 횟수로 지정한다. if (lastSelectGene[i].Count < SIMONConstants.GENE_REAL_SELECT_NUM) selectedCount = lastSelectGene[i].Count; if (selectedCount <= 0) continue; //실제 유전시킬 유전자 갯수만큼 랜덤값을 통한 추출 while (selectedIdxListIndex < selectedCount) { Random rand = new Random(); int selectIdx = rand.Next(0, selectedCount); bool retryFlag = false; for (int j = 0; j < selectedIdxListIndex; j++) { if (selectIdx == selectedIdxList[j]) { retryFlag = true; break; } } if (retryFlag) continue; selectedDNA[i].Add(lastSelectGene[i][selectIdx].dna); selectedIdxList[selectedIdxListIndex++] = selectIdx; } } //selectedDNA 리턴. return selectedDNA; }