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.

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.

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.

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.

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.