Description: 遗传算法和神经网络混合编程,可用于优化计算,仅供参考-Genetic algorithm and neural network hybrid programming, can be used to optimize the calculation, for reference only Platform: |
Size: 4096 |
Author:蒋立勇 |
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Description: 基于遗传和蚁群混合算法的图像配准算法
需要利用matlab遗传工具箱-Based on genetic and ant colony algorithm for hybrid image registration algorithm need to use genetic toolbox matlab Platform: |
Size: 3072 |
Author:凌志刚 |
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Description: 混合遗传算法用于卫星自适应调零天线研究,好东西,未来发展的方向-Hybrid genetic algorithm for the adaptive nulling antenna satellite research, good things, the direction of future development Platform: |
Size: 397312 |
Author:libin |
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Description: 矩形条带装箱问题(RSPP)是指将一组矩形装入在一个宽度固定高度不限的矩形容器中,以期
获得最小装箱高度.RSPP理论上属于NP难问题,在新闻组版、布料下料以及金属切割等工业领域中
有着广泛的应用.为解决该问题,采用了一种混合算法,即将一种新的启发式算法—— 动态匹配算
法—— 与遗传算法结合起来.混合算法中,动态匹配算法能根据4类启发式规则动态选择与装填区域相
匹配的下一个待装矩形,同时将装箱后所需容器高度用遗传算法的进化策略进行优化.对2组标准测试
问题的计算结果表明,相对于文献中的已有算法,提出的算法更加有效.-Given a set of small rectangular pieces of different sizes and a rectangular container of fixed
width and infinite length,the rectangular strip packing problem (RSPP) consists of orthogonally
placing all the pieces within the container,without overlapping,such that the overall length of the
packing is minimized. RSPP belongs to NP—hard problem in theory and has many industrial
applications such as the composition of news,the cutting of elothing and the cutting of metal,etc.To
solve the rectangular strip packing problem ,a hybrid algorithm ,which combines a novel heuristic
algorithm- dynamic—fit heuristic algorithm (DFHA),with the genetic algorithm,is adopted. The
DFHA algorithm can dynamically select the better-fit rectangle for packing,according to the width—fit
first(W FF)rule,the height—fit first(HFF) rule,the placeable first(PF) rule,and the biggest—
rectangle first(BRF)rule,and the evolutionary capability of genetic algorithm is used to optimize the
height of the packing Platform: |
Size: 477184 |
Author:郭事业 |
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Description: 现代最优化算法(有170多页的PPT,2010年的)
分为三个部分
Part 1 概论
Part 2 模拟退火算法
Part 3 遗传算法
现在常用的优化算法
禁忌搜索算法
模拟退火算法
遗传算法
人工神经网络
蚁群算法
粒子群算法
混合算法-Modern optimization algorithm is divided into three parts Part 1 Part 2 Introduction Part 3 simulated annealing genetic algorithm optimization is now commonly used in tabu search algorithm simulated annealing genetic algorithm, artificial neural network hybrid particle swarm ant colony optimization algorithm Platform: |
Size: 4639744 |
Author:zhang |
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Description: A Hybrid Genetic Algorithm for the Student-
Aware University Course Timetabling Problem Platform: |
Size: 3098624 |
Author:Hatem najjar |
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Description: 在传统遗传算法的基础上,引入FFD、BF近似算法的思想,提出求解该类问题的混合遗传算法。-In the traditional genetic algorithm, based on the introduction of FFD, BF approximation algorithm ideas put forward to solve such problems of hybrid genetic algorithms. Platform: |
Size: 261120 |
Author:刘峰 |
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Description: hybrid Archive-based Micro Genetic Algorithm (AMGA) on
a set of bound-constrained synthetic test problems is reported.
The hybrid AMGA proposed in this paper is a combination of a
classical gradient based single-objective optimization algorithm
and an evolutionary multi-objective optimization algorithm. Platform: |
Size: 401408 |
Author:nugroho |
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Description: The Class Scheduling Problem (CSP) is concerned with assigning a number
of courses corresponding to various students and instructors to classrooms
taking into consideration constraints like classroom capacities and
university regulations. The problem also attempts to optimize the
performance criteria and distribute the courses fairly to classrooms
depending on the ratio of classroom capacities to course enrollments. The
problem is a classical scheduling problem and considered to be NP‐
complete. It has received some research during the past few years given its
wide use in colleges and universities. Several formulations and algorithms
have been proposed to solve scheduling problems, most of which are based
on local search techniques.
In this project, we use a hybrid algorithm which combines features of
Genetic algorithm and Branch and Bound algorithm to solve the class
scheduling problem.
-The Class Scheduling Problem (CSP) is concerned with assigning a number
of courses corresponding to various students and instructors to classrooms
taking into consideration constraints like classroom capacities and
university regulations. The problem also attempts to optimize the
performance criteria and distribute the courses fairly to classrooms
depending on the ratio of classroom capacities to course enrollments. The
problem is a classical scheduling problem and considered to be NP‐
complete. It has received some research during the past few years given its
wide use in colleges and universities. Several formulations and algorithms
have been proposed to solve scheduling problems, most of which are based
on local search techniques.
In this project, we use a hybrid algorithm which combines features of
Genetic algorithm and Branch and Bound algorithm to solve the class
scheduling problem.
Platform: |
Size: 41984 |
Author:masmur |
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Description: 针对传统的BP或GA对模糊神经网络的识别应用存在收敛容易陷入局部极小 识别率低下等问题 提出一
种基于BFGS的混合遗传算法 其基本思想为 首先构造一种前馈型模糊神经网络结构 然后用遗传算法进化若干代
后 当目标函数的梯度或者范数小于预先设定值 则改用BFGS算法进行优化识别 仿真实验表明 对比GA该算法
收敛速度较快 识别精度提高了约7% 能够较好地应用于一类模糊神经网络的识别-In traditional BP or GA to identify the application of fuzzy neural network in convergence of easily falling into local minimum problem of low recognition rate is proposed
A hybrid genetic algorithm based on BFGS and its basic idea is first to construct a feedforward fuzzy and genetic algorithm is used to evolve neural network structure for several generations
When the gradient of the objective function or norm less than the preset value is used to optimize BFGS algorithm recognition experiment compared the algorithm GA
The recognition accuracy of fast convergence speed is improved about 7 recognition can be applied to a class of fuzzy neural networks Platform: |
Size: 732160 |
Author:renxiuju |
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