Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annea...Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.展开更多
According to the researches on theoretic basis in part Ⅰ of the paper, the spanning tree algorithms solving the maximum independent set both in even network and in odd network have been developed in this part, part ...According to the researches on theoretic basis in part Ⅰ of the paper, the spanning tree algorithms solving the maximum independent set both in even network and in odd network have been developed in this part, part Ⅱ of the paper. The algorithms transform first the general network into the pair sets network, and then decompose the pair sets network into a series of pair subsets by use of the characteristic of maximum flow passing through the pair sets network. As for the even network, the algorithm requires only one time of transformation and decomposition, the maximum independent set can be gained without any iteration processes, and the time complexity of the algorithm is within the bound of O(V3). However, as for the odd network, the algorithm consists of two stages. In the first stage, the general odd network is transformed and decomposed into the pseudo-negative envelope graphs and generalized reverse pseudo-negative envelope graphs alternately distributed at first; then the algorithm turns to the second stage, searching for the negative envelope graphs within the pseudo-negative envelope graphs only. Each time as a negative envelope graph has been found, renew the pair sets network by iteration at once, and then turn back to the first stage. So both stages form a circulation process up to the optimum. Two available methods, the adjusting search and the picking-off search are specially developed to deal with the problems resulted from the odd network. Both of them link up with each other harmoniously and are embedded together in the algorithm. Analysis and study indicate that the time complexity of this algorithm is within the bound of O(V5).展开更多
对于无向连通图G(V,E),若存在一个单映射f:V(G)∪E(G)→{1,2,…,|V|+|E|},如果uv∈E(G)且d(u)=d(v),有S(u)=S(v),其中S(u)=f(u)+∑/uz∈E(G)f(uz),d(u)表示点u的度,则称f为G的邻点可约全标号(adjacent vertex reducible total labeling,...对于无向连通图G(V,E),若存在一个单映射f:V(G)∪E(G)→{1,2,…,|V|+|E|},如果uv∈E(G)且d(u)=d(v),有S(u)=S(v),其中S(u)=f(u)+∑/uz∈E(G)f(uz),d(u)表示点u的度,则称f为G的邻点可约全标号(adjacent vertex reducible total labeling,AVRTL)。结合遗传算法和粒子群算法设计一种启发式搜索算法,可以判断有限点内随机图是否存在AVRTL。通过对实验结果分析,总结了若干联图的定理并给出证明。得到结论:如果子图G_(1)和G_(2)是AVRTL图,则图运算↑ab具有封闭性,即联图G_(1)↑_(ab)G_(2)亦为AVRTL图。展开更多
目的识别结直肠癌患者治疗后不良结局的直接与间接影响因素,并探讨这些因素与不良结局之间的因果效应,为改善患者不良结局提供依据。方法收集2013年至2015年在哈尔滨医科大学附属肿瘤医院入院并确诊为结直肠癌的患者病例信息,将治疗后...目的识别结直肠癌患者治疗后不良结局的直接与间接影响因素,并探讨这些因素与不良结局之间的因果效应,为改善患者不良结局提供依据。方法收集2013年至2015年在哈尔滨医科大学附属肿瘤医院入院并确诊为结直肠癌的患者病例信息,将治疗后两年内发生死亡、转移或复发定义为不良结局。以快速等价贪婪搜索算法构建因果图模型并分析不良结局的直接与间接影响因素,在此基础上采用无因果图时的干预演算(intervention calculus when the directed acyclic graph is absent,IDA)算法评估影响因素对不良结局的因果效应。结果共纳入2332例患者,平均年龄(68.0±10.9)岁,不良结局发生率6.22%。因果图包含20个节点、36条边;不良结局发生的直接影响因素包括化疗、病理类型、手术治疗及住院天数(|IDA|分别为0.039、0.059、0.255、0.054);间接影响因素包括年龄、饮酒、身体质量指数、分化程度、放疗、手术性质(|IDA|分别为0.011、0.021、0.012、0.042、0.021、0.030)。结论在因果图识别结直肠癌不良结局的关键因素基础上,IDA算法可量化影响因素对不良结局的因果效应。研究提示在结直肠癌的临床治疗中,提高无手术、化疗禁忌症患者的手术及化疗接受率可降低不良结局发生率,从而改善预后。展开更多
基金the National Natural Science Foundation of China (60373089, 60674106, and 60533010)the National High Technology Research and Development "863" Program (2006AA01Z104)
文摘Evolutionary computation techniques have mostly been used to solve various optimization problems, and it is well known that graph isomorphism problem （GIP） is a nondeterministic polynomial problem. A simulated annealing （SA） algorithm for detecting graph isomorphism is proposed, and the proposed SA algorithm is well suited to deal with random graphs with large size. To verify the validity of the proposed SA algorithm, simulations are performed on three pairs of small graphs and four pairs of large random graphs with edge densities 0.5, 0.1, and 0.01, respectively. The simulation results show that the proposed SA algorithm can detect graph isomorphism with a high probability.
文摘According to the researches on theoretic basis in part Ⅰ of the paper, the spanning tree algorithms solving the maximum independent set both in even network and in odd network have been developed in this part, part Ⅱ of the paper. The algorithms transform first the general network into the pair sets network, and then decompose the pair sets network into a series of pair subsets by use of the characteristic of maximum flow passing through the pair sets network. As for the even network, the algorithm requires only one time of transformation and decomposition, the maximum independent set can be gained without any iteration processes, and the time complexity of the algorithm is within the bound of O(V3). However, as for the odd network, the algorithm consists of two stages. In the first stage, the general odd network is transformed and decomposed into the pseudo-negative envelope graphs and generalized reverse pseudo-negative envelope graphs alternately distributed at first; then the algorithm turns to the second stage, searching for the negative envelope graphs within the pseudo-negative envelope graphs only. Each time as a negative envelope graph has been found, renew the pair sets network by iteration at once, and then turn back to the first stage. So both stages form a circulation process up to the optimum. Two available methods, the adjusting search and the picking-off search are specially developed to deal with the problems resulted from the odd network. Both of them link up with each other harmoniously and are embedded together in the algorithm. Analysis and study indicate that the time complexity of this algorithm is within the bound of O(V5).
文摘对于无向连通图G(V,E),若存在一个单映射f:V(G)∪E(G)→{1,2,…,|V|+|E|},如果uv∈E(G)且d(u)=d(v),有S(u)=S(v),其中S(u)=f(u)+∑/uz∈E(G)f(uz),d(u)表示点u的度,则称f为G的邻点可约全标号(adjacent vertex reducible total labeling,AVRTL)。结合遗传算法和粒子群算法设计一种启发式搜索算法,可以判断有限点内随机图是否存在AVRTL。通过对实验结果分析,总结了若干联图的定理并给出证明。得到结论:如果子图G_(1)和G_(2)是AVRTL图,则图运算↑ab具有封闭性,即联图G_(1)↑_(ab)G_(2)亦为AVRTL图。
文摘目的识别结直肠癌患者治疗后不良结局的直接与间接影响因素,并探讨这些因素与不良结局之间的因果效应,为改善患者不良结局提供依据。方法收集2013年至2015年在哈尔滨医科大学附属肿瘤医院入院并确诊为结直肠癌的患者病例信息,将治疗后两年内发生死亡、转移或复发定义为不良结局。以快速等价贪婪搜索算法构建因果图模型并分析不良结局的直接与间接影响因素,在此基础上采用无因果图时的干预演算(intervention calculus when the directed acyclic graph is absent,IDA)算法评估影响因素对不良结局的因果效应。结果共纳入2332例患者,平均年龄(68.0±10.9)岁,不良结局发生率6.22%。因果图包含20个节点、36条边;不良结局发生的直接影响因素包括化疗、病理类型、手术治疗及住院天数(|IDA|分别为0.039、0.059、0.255、0.054);间接影响因素包括年龄、饮酒、身体质量指数、分化程度、放疗、手术性质(|IDA|分别为0.011、0.021、0.012、0.042、0.021、0.030)。结论在因果图识别结直肠癌不良结局的关键因素基础上,IDA算法可量化影响因素对不良结局的因果效应。研究提示在结直肠癌的临床治疗中,提高无手术、化疗禁忌症患者的手术及化疗接受率可降低不良结局发生率,从而改善预后。
