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- Probability gate quantum evolution algorithm 概率门量子进化算法
- parallel quantum evolution algorithm 并行量子进化算法
- immune quantum evolution algorithm 免疫量子进化算法
- Quantum Evolutionary Algorithm(QEA) is a distinctive type of algorithm for optimization currently,and the theoretical basis of QEA is quantum computation. 摘要 量子进化算法(QEA)是目前较为独特的优化算法,它的理论基础是量子计算。
- In order to optimize the complex functions, a real-coded quantum evolutionary algorithm is proposed based on the relational concepts and principles of quantum computing. 摘要为求解复杂函数优化问题,基于量子计算的相关概念和原理,提出一种实数编码量子进化算法。
- Quantum Evolution Algorithm Based on Descending Search 基于下降搜索的量子进化算法
- Parallel Immune Quantum Evolution Algorithm Based on Learning Mechanism 基于学习的并行免疫量子进化算法
- immune quantum evolutionary algorithm 免疫量子进化算法
- Quantum Evolutionary Algorithm (QEA) 量子进化算法
- Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid quantum evolutionary algorithms (QEA) based on combining QEA with particle swarm optimization (PSO). 摘要将量子进化算法(QEA)和粒子群算法(PSO)互相结合,提出了两种混合量子进化算法。
- Inspired by the idea of hybrid optimization algorithms,this paper proposes two hybrid Quantum Evolutionary Algorithms(QEA) based on combining QEA with Particle Swarm Optimization(PSO). 文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合,提出了两种混合量子进化算法。
- Quantum evolution algorithm 量子进化算法
- Parallel Immune Quantum Evolutionary Algorithm Based on Learning Mechanism and Its Convergence 基于学习的并行免疫量子进化算法及收敛性
- quantum evolutionary algorithm 量子进化算法
- Hybrid Quantum Evolutionary Algorithms and Its Application in Multiuser Detection 混合量子进化算法及其在多用户检测中的应用
- Hybrid quantum inspired evolution algorithm 混合量子进化算法
- Quantum inspired evolution algorithm 量子进化算法
- IDEP algorithm also takes the network training process as an optimization problem. Differing from CO method, IDEP algorithm is based on the differential evolution algorithm. 算法也将网络训练作为一个优化问题来处理,与有约束优化方法不同的是,它是建立在差分进化算法的基础上,通过对一个群体不断进行选择、杂交和变异操作,使之逐步进化,最终找到优化解。
- The idea of evolutionary algorithms is not new. 进化算法这个观点并不是新提出。
- We formulate the design problem as a constrained parametric optimization and apply a differential evolution algorithm to search globally the optimal controller parameters. 此设计问题将表示成含条件式之参数最适化,而微分演化演算法将应用于搜寻全域之最适控制参数。