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   共同进化计算 的翻译结果: 查询用时:0.013秒
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共同进化计算
相关语句
  co-evolutionary computing
     First,the co-evolutionary computing based path planning model is built by mapping the UAV paths to the evolutionary individuals,then the individuals' fitness computing is mainly discussed.
     将航路映射到进化计算个体建立了基于共同进化计算的航路规划问题模型,以此为基础重点讨论了个体适应度设计等关键问题。
短句来源
  co-evolution computation
     Algorithm of Classification Rules Based on Co-Evolution Computation
     基于共同进化计算的分类规则算法
短句来源
     In order to improve the accuracy of classified mining,the ID3,C4.5 and EC(Evolution Computation) algorithms are analyzed,and two co-evolution populations are designed to respectively describe the attribute set and the classification rule set. The algorithm of the classification rules based on the co-evolution computation(CRCEC) and its fitness function are then proposed.
     为提高分类挖掘的准确度,在分析ID3,C4.5和进化算法(EC)的基础上,设计了两个共同进化的种群分别表示选择的属性子集和分类规则子集,提出基于共同进化计算的分类规则算法(CRCEC),并构建CRCEC算法的适应度评价函数.
短句来源
  相似匹配句对
     We also give a new correction factor to simplify the calculation of the Unstatistic.
     的计算
短句来源
     Algorithm of Classification Rules Based on Co-Evolution Computation
     基于共同进化计算的分类规则算法
短句来源
     Mobile Computing
     移动计算
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Task allocation and scheduling plays a key role in network computing environment and makes a notable impact on the overall performance. Scheduling problems are known to be in general NP complete, only sub optimal can be obtained by classical scheduling approaches in most cases. Though the conventional single population based genetic algorithms (CSGAs) can find solutions with better quality than classical approaches for scheduling problems, the efficacy and efficiency of CSGA decrease with the increase of...

Task allocation and scheduling plays a key role in network computing environment and makes a notable impact on the overall performance. Scheduling problems are known to be in general NP complete, only sub optimal can be obtained by classical scheduling approaches in most cases. Though the conventional single population based genetic algorithms (CSGAs) can find solutions with better quality than classical approaches for scheduling problems, the efficacy and efficiency of CSGA decrease with the increase of number of tasks in parallel and distributed systems. By analyzing the mechanism that makes CSGA in scalable, this paper firstly proposed a task allocation and scheduling algorithm based on computational model of cooperative coevolution, which is inspired by the coevolutionary phenomena of natural species. Then, some related work is discussed, including permutation representation of chromosome, genetic operators such as improved crossover, internal crossover and migration as a kind of mutation, cooperative interactions among species by selecting the best individual as its cooperative representative, combination of sub schedules into a whole, and fitness computation of individuals. The algorithm was analyzed mathematically, which shows that the exponential increase index of the coevolution based scheduling algorithm is higher than that of CSGA. At last, simulation results of the proposed algorithm and CSGA are given, which verify the theoretical results, and show that the convergence and optimal/sub optimal of proposed algorithm are better than that of CSGA. The algorithm is of practical use in engineering.

并行与分布式计算环境中随着独立任务的增多 ,传统进化类单种群的任务分配与调度算法的效率与效力随之大为降低 .该文在分析传统解完整编码单种群进化类算法的基础上 ,基于生物界多物种间共同进化的机制提出了任务分配与调度的合作式共同进化计算模型 ,并探讨了任务分配与调度问题中的子种群合作方式与个体的适应值计算方法 .此外 ,从数学上分析了基于合作式共同进化的任务分配与调度算法的性能 ,指出共同进化调度方法中好的调度方案能以高于传统单种群进化算法的递增指数递增 .仿真分析证实了算法的理论分析结果 ,算法具有实际工程价值

In order to improve the accuracy of classified mining,the ID3,C4.5 and EC(Evolution Computation) algorithms are analyzed,and two co-evolution populations are designed to respectively describe the attribute set and the classification rule set.The algorithm of the classification rules based on the co-evolution computation(CRCEC) and its fitness function are then proposed.Moreover,a comparison among EC,ID3,C4.5 and CRCEC algorithms is carried out using four datasets of University of California,Irvine.The results...

In order to improve the accuracy of classified mining,the ID3,C4.5 and EC(Evolution Computation) algorithms are analyzed,and two co-evolution populations are designed to respectively describe the attribute set and the classification rule set.The algorithm of the classification rules based on the co-evolution computation(CRCEC) and its fitness function are then proposed.Moreover,a comparison among EC,ID3,C4.5 and CRCEC algorithms is carried out using four datasets of University of California,Irvine.The results show that the proposed CRCEC algorithm is of high accuracy and helps obtain rules that are simple and easy to understand.The proposed algorithm is finally applied to the predictive system of highway charge as an application example.

为提高分类挖掘的准确度,在分析ID3,C4.5和进化算法(EC)的基础上,设计了两个共同进化的种群分别表示选择的属性子集和分类规则子集,提出基于共同进化计算的分类规则算法(CRCEC),并构建CRCEC算法的适应度评价函数.用4个加利福尼亚大学Ir-vine分校的数据集对CRCEC,ID3,C4.5和EC算法进行测试比较,结果表明CRCEC算法分类准确度高,可以得到简洁的、可理解性强的规则.最后给出了CRCEC算法在公路车辆征费分类预测系统中的一个应用实例.

To resolve the path design problem for cooperative UAVs,a new co-evolutionary computing based path planning method is proposed.First,the co-evolutionary computing based path planning model is built by mapping the UAV paths to the evolutionary individuals,then the individuals' fitness computing is mainly discussed.The method is simulated under a scenario of suppression of enemy air defense.The simulation results show that the method can quickly find cooperative paths for UAVs,and can flexibly handle several factors,such...

To resolve the path design problem for cooperative UAVs,a new co-evolutionary computing based path planning method is proposed.First,the co-evolutionary computing based path planning model is built by mapping the UAV paths to the evolutionary individuals,then the individuals' fitness computing is mainly discussed.The method is simulated under a scenario of suppression of enemy air defense.The simulation results show that the method can quickly find cooperative paths for UAVs,and can flexibly handle several factors,such as fuel,safety,collision-avoiding and cooperation.The results also show that the algorithm converges quickly and has good linear time-performance.

面向多无人机协同作战的航路设计问题,提出了一种较新颖的多无人机协同航路规划共同进化方法。将航路映射到进化计算个体建立了基于共同进化计算的航路规划问题模型,以此为基础重点讨论了个体适应度设计等关键问题。以SEAD任务为想定,对多无人机协同航路规划共同进化方法进行了仿真。仿真结果表明该方法能够快速为多无人机找到协同航路,能够综合考虑无人机航路的燃油、安全、避碰以及任务协同等指标,可以解决多无人机协同执行任务的航路设计问题,且具有较好的收敛性和线性时间性。

 
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