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Description: 实现用固定变异概率和自适应变异概率解tsp问题的比较，自适应式算法采用基于种群差异度的自适应算法，详见实验报告-achieve fixed mutation probability and Adaptive Solutions tsp mutation probability of comparison, Adaptive Algorithm-based differences in the populations adaptive algorithm, as detailed experimental report
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Size: 86221 |
Author: 叶靥 |
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Description: 物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location
Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP)
Networks: Shortest path, min cost network flow, minimum spanning tree problems
Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting
Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP
Material handling: Equipment selection
General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure
Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes -logistics analysis tool kit. Facility location : Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated Vehicle routing facility location : VRP, VRP with time windows, the traveling salesman problem (TSP) Networks : Shortest path, min cost network flow, minimum spanning tree Geocoding problems : world city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting Layout : Steepest descent Pairwise interchange (Constituencies) heuristic for QAP Material handling : Equipment selection General purpose : Linear programming using the revised simplex method, mixed-integer linear programming programming (MILP) branch and bound procedure Data : world cities with populations of at least 10,000, U.
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Size: 4853791 |
Author: 陈宝文 |
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Description: 在页面中实时转换简繁体，使您的网站适合所有的人群。-pages in real-time conversion Brief History, your site is suitable for all populations.
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Size: 101649 |
Author: feigo |
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Description: 本算法中采取了种群规模为100，同时采用轮盘赌来获取种群。开始使用随机的方法得到初始的种群-the algorithm adopted a population size of 100, using roulette to access populations. Using the stochastic method initial Stocks
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Size: 2770 |
Author: 谢继晖 |
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Description: 本算法中采取了种群规模为100，同时采用轮盘赌来获取种群。开始使用随机的方法得到初始的种群-the algorithm adopted a population size of 100, using roulette to access populations. Using the stochastic method initial Stocks
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Size: 2048 |
Author: 谢继晖 |
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Description: 实现用固定变异概率和自适应变异概率解tsp问题的比较，自适应式算法采用基于种群差异度的自适应算法，详见实验报告-achieve fixed mutation probability and Adaptive Solutions tsp mutation probability of comparison, Adaptive Algorithm-based differences in the populations adaptive algorithm, as detailed experimental report
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Size: 2419712 |
Author: 叶靥 |
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Description: 物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location
Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP)
Networks: Shortest path, min cost network flow, minimum spanning tree problems
Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting
Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP
Material handling: Equipment selection
General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure
Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes -logistics analysis tool kit. Facility location : Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated Vehicle routing facility location : VRP, VRP with time windows, the traveling salesman problem (TSP) Networks : Shortest path, min cost network flow, minimum spanning tree Geocoding problems : world city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting Layout : Steepest descent Pairwise interchange (Constituencies) heuristic for QAP Material handling : Equipment selection General purpose : Linear programming using the revised simplex method, mixed-integer linear programming programming (MILP) branch and bound procedure Data : world cities with populations of at least 10,000, U.
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Size: 4853760 |
Author: 陈宝文 |
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Description: 在页面中实时转换简繁体，使您的网站适合所有的人群。-pages in real-time conversion Brief History, your site is suitable for all populations.
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Size: 101376 |
Author: feigo |
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Description: ％ [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation)
％ Finds a maximum of a function of several variables.
％ fmaxga solves problems of the form:
％ max F(X) subject to: LB <= X <= UB
％ BestPop--------最优的群体即为最优的染色体群
％ Trace----------最佳染色体所对应的目标函数值
％ FUN------------目标函数
％ LB-------------自变量下限
％ UB-------------自变量上限
％ eranum---------种群的代数,取100--1000(默认1000)
％ popsize--------每一代种群的规模；此可取50--100(默认50)
％ pcross---------交叉的概率,此概率一般取0.5--0.85之间较好(默认0.8)
％ pmutation------变异的概率,该概率一般取0.05-0.2左右较好(默认0.1)
％ options--------1×2矩阵,options(1)=0二进制编码(默认0),option(1)~=0十进制编码,option(2)设定求解精度(默认1e-4)- [BestPop, Trace] = fmaxga (FUN, LB, UB, eranum, popsize, pcross, pmutation) Finds a maximum of a function of several variables. Fmaxga solves problems of the form: max F (X) subject to : LB <= X <= UB BestPop-------- optimal chromosome groups is the best group Trace---------- chromosome corresponding to the best objective function value FUN------------ objective function LB------------- variable lower limit since the UB------------- variable upper limit eranum--------- populations algebra, take 100- 1000 (default 1000) popsize-------- population size of each generation this desirable 50- 100 (default 50) pcross--------- crossover probability, the probability of a general check 0.5- 0.85 between the better (default 0.8) pmutation------ mutation probability, the probability of 0.05 general admission better about-0.2 (default 0.1) options-------- 1 × 2 matrix, options (1) = 0 binary code (default 0), option (1) ~ = 0 decimal coding, option (2 ) set accuracy (default 1e-4)
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Size: 3072 |
Author: mmcc |
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Description: GA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。
本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值啦）
并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；-GA (Simple Genetic Algorithm) is a powerful, intelligent multi-variable optimization algorithms, which mimic the breeding populations of the law to be optimized. SGA can optimize this variable, and the minimum value, maximum value (when the function of the countdown you will seek the minimum value) and to support the floating-point encoding, grey code, binary code round of gambling options, tournament selection single-point crossover, uniform Cross, floating-point crossover single point mutation, floating-point variation
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Size: 9216 |
Author: yuandi |
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Description: 本遗传算法是保留大量状态种群的随机爬山搜索算法，新的状态通过变异和杂交产生，杂交把来自种群的状态对结合在一起。-The genetic algorithm is to retain a large number of the state of stocks climbing random search algorithm, the new state generated through mutation and hybrids, hybrid populations from the state of the combination.
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Size: 71680 |
Author: 同承 |
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Description: 实现了一个简单的花朵进化的模拟过程。
花朵的种群数量是10，共进化了50代。
通过运行程序，你会发现通过不断的进化，种群的总的适应环境的能力在逐步提高（fitness的值下降）。
-The realization of a simple simulation of the evolution of flowers. Flower populations of 10, a total of 50 on behalf of evolution. By running the program, you will find that through continuous evolution of the general population s ability to adapt to the environment gradually increase in the (fitness value decrease).
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Size: 2048 |
Author: 陈石 |
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Description: 遗传算法(Genetic Algorithm,GA)是一种抽象于生物进化过程的基于自然选择和生物遗传机制的优化技术.
遗传算法的基本原理
在遗传算法的执行过程中,每一代有许多不同的种群个体(染色体 )同时存在。这些染色体中哪个保留(生存)、哪个淘汰(死亡),是根据 它们对环境的适应能力来决定的,适应性强的有更多的机会保留下来 。适应性强弱是通过计算适应性函数f(x)的值来判别的,这个值称为适应值。适应值函数f(x)的构成与目标函数有密切关系,往往是目标函数的变种。-Genetic Algorithm (Genetic Algorithm, GA) is an abstract in the process of biological evolution based on natural selection and genetic mechanisms of biological optimization technology. The basic principles of genetic algorithm genetic algorithm in the implementation process, each generation has a number of different populations of individuals ( chromosome) at the same time. Which of these chromosomes reservation (survival), which eliminated (death), is based on their ability to adapt to the environment to decide adaptable have more opportunities to retain it. Adaptation strength is by calculating the adaptive function f (x) to determine the value, this value is called fitness. Fitness function f (x) the composition and the objective function is closely related to the objective function is often a variant.
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Size: 8192 |
Author: fk774 |
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Description: 实现了一个简单的花朵进化的模拟过程。
花朵的种群数量是10，共进化了50代。
-The realization of a simple simulation of the evolution of flowers. Flower populations is 10, a total of 50 on behalf of evolution.
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Size: 6144 |
Author: sp |
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Description: PEnna生物模型中的死亡和繁殖规律，运用此规律可以模拟生物种群的数量。-Penna biological model of death and reproduction of the law, the application of this law can simulate the number of biological populations.
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Size: 2048 |
Author: 张小伟 |
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Description: 一系列好用的用户友好的启发式优化算法，包括非自适应算法，基于模拟退火算法的种群算法，基本遗传算法，差分进化算法以及粒子群优化算法。此外，也包括神圣算法，它利用了所有这些优化算子，虽然有时交换种群之间的不同算法。-A nice set of user-friendly heuristic optimizers. Included are a non-adaptive, population based Simulated Annealing algorithm, a basic Genetic Algorithm, (transversal) Differential Evolution algorithm and Particle Swarm Optimization algorithm. Also, the GODLIKE-algorithm is included, which simply uses all of these optimizers while occasionally swapping populations between the different algorithms.
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Size: 26624 |
Author: 竹子的信仰 |
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Description: 用4个种群来优化函数，每次取三个种群里面的最佳放入第四种群，经过反复迭代后取得函数的最佳值-4 used to optimize the function of populations, each from three of the best stocks inside Add the fourth population, after repeated iterations of the optimal value function
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Size: 16384 |
Author: 史峰 |
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Description: Short description: GUI Ant-Miner is a tool for extracting classification rules from data. It is an updated version of a data mining algorithm called Ant-Miner (Ant Colony-based Data Miner), which was proposed in 2002 by Parpinelli, Lopes and Freitas. GUI Ant-Miner differs from the original algorithm as follows: It has a friendly graphical user interface, makes possible the use of ant populations within the Ant Colony Optimization (ACO) concept, data input file is standardized with the well-known Weka system, and runs on virtually any operating system since it is written in Java.
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Size: 72704 |
Author: xiaochuzhe |
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Description: 遗传算法入门实例一：PID参数的优化[v1.0]
本文件夹包含：
图片IMG_0084 和IMG_0086为实验照片
IMG_0084为初始种群中某个体的PID调整效果
IMG_0086为进化了N(到底多少代我也没有去数)代之后的PID调整效果 文件GA为正文
源码\GA\ 为实验代码，WINAVR20060421+AVR Studio 4.12-Introduction example of a genetic algorithm: PID parameters optimization [v1.0] This folder contains: Picture IMG_0084 and IMG_0086 IMG_0084 photos for the experimental populations for the initial adjustment of an individual effect IMG_0086 PID for the evolution of the N (in the end too much for me did not go to a few) on behalf of the after effects of PID adjustment for the body of the source file GA \ GA \ as experimental code, WINAVR20060421+ AVR Studio 4.12
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Size: 2831360 |
Author: jiaoyfmagical |
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Description: Chaotic populations in genetic algorithms
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Size: 1219584 |
Author: azarakhsh |
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