{"title":"0/1背包问题的DNA计算模型","authors":"Aili Han","doi":"10.1109/HIS.2006.21","DOIUrl":null,"url":null,"abstract":"We have devised a DNA encoding method and a corresponding DNA algorithm for the 0/1 knapsack problem. Suppose that item set I={1,2 ... n}, profit set P={p_1,p_2,...,p_n}, weight set W={w_1,w_2,...,w_n}, and knapsack capacity is c. We use two DNA strands s_i1 and s_i2 to encode each item i, where the DNA strand s_i1 is with a length of wi whose center part is with a length of p_i, and the DNA strand s_i2 is the reverse complement of the center part of s_i1. For any two items i,j we add one DNA strand s_aij as an additional code, which is the reverse complement of the last part of s_i1 and the first part of s_j1. The proposed DNA encoding method is an improvement on the previous ones, and it provides further evidence for the ability of DNA computing to solve numerical optimization problems.","PeriodicalId":150732,"journal":{"name":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"DNA Computing Model for the 0/1 Knapsack Problem\",\"authors\":\"Aili Han\",\"doi\":\"10.1109/HIS.2006.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have devised a DNA encoding method and a corresponding DNA algorithm for the 0/1 knapsack problem. Suppose that item set I={1,2 ... n}, profit set P={p_1,p_2,...,p_n}, weight set W={w_1,w_2,...,w_n}, and knapsack capacity is c. We use two DNA strands s_i1 and s_i2 to encode each item i, where the DNA strand s_i1 is with a length of wi whose center part is with a length of p_i, and the DNA strand s_i2 is the reverse complement of the center part of s_i1. For any two items i,j we add one DNA strand s_aij as an additional code, which is the reverse complement of the last part of s_i1 and the first part of s_j1. The proposed DNA encoding method is an improvement on the previous ones, and it provides further evidence for the ability of DNA computing to solve numerical optimization problems.\",\"PeriodicalId\":150732,\"journal\":{\"name\":\"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HIS.2006.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIS.2006.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We have devised a DNA encoding method and a corresponding DNA algorithm for the 0/1 knapsack problem. Suppose that item set I={1,2 ... n}, profit set P={p_1,p_2,...,p_n}, weight set W={w_1,w_2,...,w_n}, and knapsack capacity is c. We use two DNA strands s_i1 and s_i2 to encode each item i, where the DNA strand s_i1 is with a length of wi whose center part is with a length of p_i, and the DNA strand s_i2 is the reverse complement of the center part of s_i1. For any two items i,j we add one DNA strand s_aij as an additional code, which is the reverse complement of the last part of s_i1 and the first part of s_j1. The proposed DNA encoding method is an improvement on the previous ones, and it provides further evidence for the ability of DNA computing to solve numerical optimization problems.