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[Software Engineering] Genetic_Algorithms_in_dam_safety_monitoring_neural DL : 0: 本文基于遗传算法思想，采用浮点数矩阵表示编码，在遗传算法的进化过程中加入一定的约束条件等方法，探讨了网络结构的设计和学习。经实例分析，在用于建立大坝安全监控预报模型的前馈神经网络设计中，该方法在满足一定约束条件下，能同时有效地寻找合适的网络结构和相应的参数(神经网络的权值和阈值)，且在精度和速度上都有较大的提高，为实现实时在线分析评价大坝的安全性态提供了有力的技术支持。-Based on the genetic algorithm, using a float matrix coding, Genetic algorithms in the evolutionary process to be bound by certain conditions, to explore the structure of the network design and learning. By analyzing the examples used in the establishment of dam safety monitoring forecasting model of neural network design, The constraint in meeting certain conditions, can effectively find suitable network structure and the corresponding parameters (the neural network weights and thresholds), and the accuracy and speed have improved greatly. To achieve real-time online analysis and evaluation of the safety of the dam states provide strong technical support.
Date : 2008-10-13 Size : 30.33kb User : 汪顺和
[Software Engineering] Genetic_Algorithms_in_dam_safety_monitoring_neural DL : 0: 本文基于遗传算法思想，采用浮点数矩阵表示编码，在遗传算法的进化过程中加入一定的约束条件等方法，探讨了网络结构的设计和学习。经实例分析，在用于建立大坝安全监控预报模型的前馈神经网络设计中，该方法在满足一定约束条件下，能同时有效地寻找合适的网络结构和相应的参数(神经网络的权值和阈值)，且在精度和速度上都有较大的提高，为实现实时在线分析评价大坝的安全性态提供了有力的技术支持。-Based on the genetic algorithm, using a float matrix coding, Genetic algorithms in the evolutionary process to be bound by certain conditions, to explore the structure of the network design and learning. By analyzing the examples used in the establishment of dam safety monitoring forecasting model of neural network design, The constraint in meeting certain conditions, can effectively find suitable network structure and the corresponding parameters (the neural network weights and thresholds), and the accuracy and speed have improved greatly. To achieve real-time online analysis and evaluation of the safety of the dam states provide strong technical support.
Date : 2025-12-28 Size : 30kb User : 汪顺和
[Software Engineering] bbbb DL : 0: 摘　要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。-Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.
Date : 2025-12-28 Size : 270kb User : 王飞
[Software Engineering] GAResource DL : 0: GA generation for introduces. We also provide some information on evolutionary algorithms used for optimization
Date : 2025-12-28 Size : 75kb User : Duy
[Software Engineering] 21595264 DL : 0: 具体包含模拟退火算法和基因进化算法的最新方法及步骤. 需要的朋友可以下载,里面详细讲述了具体的实现方法和过程, 很有实际意义.-It contains the simulated annealing algorithm and genetic evolutionary algorithms latest methods and procedures. Friend in need can be downloaded, which describe in detail the specific method and process of great practical significance.
Date : 2025-12-28 Size : 3.33mb User : yfas
[Software Engineering] PSO-matlab DL : 0: 粒子群算法源程序，是近年来发展起来的一种新的进化算法。有实现容易、精度高、收敛快等优点。是一种并行算法。-Particle swarm algorithm source code, is a new evolutionary algorithm developed in recent years. There are easy to implement, high precision, fast convergence and so on. Is a kind of parallel algorithms.
Date : 2025-12-28 Size : 5kb User : Mickel
[Software Engineering] A-hybrid-cuckoo-search-and-genetic-algorithm-for- DL : 0: Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.-Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.
Date : 2025-12-28 Size : 421kb User : fifo_enp
[Software Engineering] 0A-study-on-intrusion-detection-using-neural-netw DL : 0: 0A study on intrusion detection using neural networks trained with evolutionary algorithms
Date : 2025-12-28 Size : 432kb User : ilyesben