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[Industry research] psoanduse DL : 0: 粒子群优化算法及其应用。详细介绍了粒子群优化算法（又叫微粒群算法）及其应用-PSO algorithm and its application. Details on the PSO algorithm (also known as PSO) and its application
Date : 2026-01-09 Size : 159kb User : sealyu
[Industry research] GA_BP DL : 0: Date : 2026-01-09 Size : 170kb User : wangnan
[Industry research] Behrouz_SSPR04_final DL : 0: K-NN algorithm in collaboration with GA research paper
Date : 2026-01-09 Size : 127kb User : amila banuka
[Industry research] v1.01 DL : 0: v.1.01 scaduling with GA plan
Date : 2026-01-09 Size : 243kb User : bayusastra
[Industry research] UsingGAforNetworkIntrusionDetection DL : 0: Using GA for Network Intrusion Detection
Date : 2026-01-09 Size : 183kb User : AmuthaS
[Industry research] Output DL : 0: a complete paper GA based feature selection
Date : 2026-01-09 Size : 1.04mb User : raju
[Industry research] pptGa5 DL : 0: basic knowldge about GA techniq.
Date : 2026-01-09 Size : 35kb User : koli
[Industry research] haitu DL : 0: 基于海图数据的AUV全局路径规划方法研究. 关键字海图遗传算法-Chart data based on global path planning for AUV Research Keywords charts GA
Date : 2026-01-09 Size : 3.07mb User : wanghui
[Industry research] A-new-approach-of-PSO-GA DL : 0: 基于PSO及遗传算法的改进，用于并行性方面的研究-PSO and Genetic Algorithm for the parallelism
Date : 2026-01-09 Size : 265kb User : zhangsan
[Industry research] ga DL : 0: GA遗传算法介绍，系统的介绍了GA的由来和方法，并对其应用进行了介绍-GA introduce
Date : 2026-01-09 Size : 536kb User : 季慧华
[Industry research] GApresentation DL : 0: Application of GA in Clustering
Date : 2026-01-09 Size : 337kb User : Yugal Kumar
[Industry research] GA DL : 0: 文章简要的介绍了遗传算法的原理，适合初学者来学习-The paper introduces the principle of genetic algorithm, suitable for beginners to learn
Date : 2026-01-09 Size : 54kb User : 李力
[Industry research] A-Novel-Multi-focus---Image--Fusion DL : 0: We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, relative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks.
Date : 2026-01-09 Size : 241kb User : fia4joy
[Industry research] BBOvsGA DL : 0: bbo and ga optimization comparision
Date : 2026-01-09 Size : 545kb User : izzeddine
[Industry research] GA-IN-WIND-POWER DL : 0: Its an IEEE paper on Optimization of Electric Distribution System of Large Offshore Wind Farm with Improved Genetic Algorithm.
Date : 2026-01-09 Size : 163kb User : kamaljeet
[Industry research] Predicting-Housing-Value DL : 0: In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement
Date : 2026-01-09 Size : 326kb User : ahmed
[Industry research] GA DL : 0: Genetic Algorithm for various optimizaton issues
Date : 2026-01-09 Size : 165kb User : dhiren
[Industry research] A-combination-of-genetic-algorithm-and-particle-s DL : 0: A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.
Date : 2026-01-09 Size : 522kb User : fifo_enp