※ 引述《freelance978 (阿什坎迪)》之銘言： : 剛剛閱讀有關Hybrid K-medoid Algorithm的文章 : 他有提到三個步驟 1選擇 2交配 3突變 : 染色體編碼方式是整數編碼(Integer Representation) : 每筆資料會被依序編號 1,2,…,n，其中 n 為資料的總筆數。 : 將每一條染色體固定長度為 K，K為分群數， : 且每一條染色體裡的每一個基因(gene)放入的可以是任何一筆資料的編號 1,2,…,n : 而這染色體的 K 個基因裡的數字，分別地代表某一群資料集的群集中心資料點之編號。 : ex:若資料集中有 10 筆資料（給予編號 1..10），欲分成 3 群(K=3)， : 其隨機產生的染色體表示假設為： 2 5 8 ，代表的意思是資料集分成三群後 : 各群中心 : 我要問的是他提到的交配跟突變方法: : 交配: 混合子集重組交配法(Mix Subset Recombination Crossover) : 突變: Flip Mutation : 有人聽過這兩個方法嗎 : 查過他的ref 還是沒寫得很清楚 : 謝謝 上面有寫啊。 以下節錄自 "A Hybrid Algorithm for K-medoid Clustering of Large Data Sets" Wrote by W. Sheng and X. Liu: In order to avoid producing illegal individuals or producing offspring identical to the parents, we apply the mix subset recombination crossover [5], which has a built-in mutation with probability of Pm,mix. Given two parent individuals X1 and X2 with k features, mix subset recombination crossover works as follows: (1) Mix X1 and X2 to obtian Xmix by appending one parent individual to another and then the features of resulting Xmix are randomly scrabled. (2) Perform flip mutation on each feature of Xmix with a predetermined probability Pm,mix and then randomly scrable the features of Xmix again. (3) Build the offspring X3 by coping features from Xmix starting at the leftmost feature and going featurewise to the right, subject to the condition that features already in X3 are skipped. (4) Build the offspring X4 by coping features from Xmix starting at the rightmost feature and going featurewise to the left. subject to the condition that features already in X4 are skipped. The crossover is applied on each paired parent with probability of Pc. After crossover, the probability Pm of flip mutation will be applied to the offspring. Flip mutation replaces the chosen feature by another randomly generated feature, subject to the restriction that the new feature is not presented in the current genotype of the individual. 哪邊看不懂？需要講解嗎？ --

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