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Description: This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
- This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
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Size: 6902 |
Author: 刘海 |
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Description: 遗传算法源代码,实现了选择操作、交叉操作和变异操作，通过适应度函数完成种群的选择及收敛。-genetic algorithm source code and realized the choice of operation, crossover and mutation operation, through fitness function completed Stocks choice and convergence.
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Size: 30590 |
Author: 李文 |
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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）。
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Size: 1893 |
Author: 陈石 |
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Description: This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
- This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
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Size: 6144 |
Author: 刘海 |
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Description: 基因算法（GA）
GA 是一种启发式的优化法 （heuristic optimization method）， 它是通过既定的随机搜索进行操作．优化问题的可能的解的集合被认为是 个体（individuals）组成的 人群（population）． 一个个体对它的环境的适应程度由它的 健康度（fitness）表示． -genetic algorithm (GA) GA is a heuristic optimization method (heuristic optimi method. 5), which is established through a random search for the operation. Optimization of the solution may be considered a collection of individuals (individuals) of the crowd (pop ulation). It's an individual adaptation to the environment by its Health (fitness).
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Size: 16384 |
Author: 黄波 |
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Description: 遗传算法源代码,实现了选择操作、交叉操作和变异操作，通过适应度函数完成种群的选择及收敛。-genetic algorithm source code and realized the choice of operation, crossover and mutation operation, through fitness function completed Stocks choice and convergence.
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Size: 30720 |
Author: 李文 |
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Description: 转载得有关利用遗传算法优化bp神经网络得源程序中得有关适应度函数编写-reproduced in the use of genetic algorithm optimization bp neural network in the source in the preparation of fitness function
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Size: 166912 |
Author: raul |
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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: 基于VB的遗传算法软件实现
在程序中,FitnessValue (i) 为适应度值数组、avFit2nessValue (100) 为归一化适应度值数组、Population2 Chrom(i ,j) 为遗传个体的等位基因值、Popsize 为种群中的个体数,CHROMLENGTH为一母体对的等位基因
总数。-VB-based genetic algorithm software implementation in the proceedings, FitnessValue (i) In order to meet an array value, avFit2nessValue (100) for the normalized fitness value of the array, Population2 Chrom (i, j) for the genetic value of individual alleles, Popsize for stocks in the number of individuals, CHROMLENGTH for one parent to the total number of alleles.
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Size: 1024 |
Author: sdf |
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Description: ga的一个仿真例子程序，还不错，用起来效果比较好，需要自己根据项目情况写适应度函数-ga a simulation example of the procedure, but also good to use effect is better, the basis of project information required to write their own fitness function
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Size: 3072 |
Author: huangchao |
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Description: 程序中包括了数据进制转换和适应度等计算的遗传算法-Procedures, including data conversion and M-ary fitness in terms of the genetic algorithm
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Size: 6144 |
Author: 刘雯雯 |
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Description: 线性二次最优控制加权阵遗传算法优化适应度函数m文件；模糊控制器量化比例因子遗传算法优化适应度函数m文件-Linear quadratic optimal control weighted array genetic algorithm fitness function m documents quantization scale factor of fuzzy controller optimized by GA fitness function m file
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Size: 4096 |
Author: 张立迎 |
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Description: Java实现的遗传算法工具集：GA Playground -The GA Playground is a general purpose genetic algorithm toolkit where the user can define and run his own optimization problems. The toolkit is implemented in the Java language, and requires (when used as an application, in its full mode), a Java compiler and a very basic programming knowledge (just enough for coding a fitness function). Defining a problem consists of creating an Ascii definition file in a format similar to Windows Ini files, and modifying the fitness function in the GaaFunction source file. In addition, other methods can (optionally) be overwritten (e.g. the drawing method), other classes can be extended or replaced, and additional input can be supplied through Ascii files.
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Size: 574464 |
Author: LaoGuan |
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Description: This program runs a GA. The roulette wheel method for parent
selection is used here. Elitism is included. Parent selection is
from the population including the elite chromosomes. The standard
bit form is used here. As usual, code works in terms of fitness
values (higher better) results, however, are reported for the loss
values of actual interest. This code does not work with constraints
on theta values other than those directly associated with thetamax
and thetamin. -This program runs a GA. The roulette wheel method for parent
selection is used here. Elitism is included. Parent selection is
from the population including the elite chromosomes. The standard
bit form is used here. As usual, code works in terms of fitness
values (higher better) results, however, are reported for the loss
values of actual interest. This code does not work with constraints
on theta values other than those directly associated with thetamax
and thetamin.
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Size: 11264 |
Author: siva |
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Description: 一、用GA直接训练BP网络的权重算法
主程序：gafault.m 它包括以下子程序：
1. BP网络初始化：nninit.m――给出P，T，R，S1，S2；
2. 适应值计算函数：gabpEval.m；
3．将遗传算法的编码解码为BP网络所对应的权值、阈值函数：gadecod.m；
二、用GA先求BP网络的权重，再用纯BP直接训练BP的混合GA－BP算法
主程序：gabpfault.m 它包括以下子程序：
1. 网络初始化：nninit.m――给出P，T，R，S1，S2；
2. 适应值计算函数：gabpEval.m；
3．将遗传算法的编码解码为BP网络所对应的权值、阈值函数：gadecod.m；
三、纯BP
主程序：（1）bpfault.m 在MATLAB5.2上
（2）bpfault.m 在MATLAB6.5上 为后来所加
-First, the direct training of BP network with GA the weight algorithm
Main program: gafault.m It includes the following routines:
1. BP network initialization: nninit.m-- given P, T, R, S1, S2
2. Fitness calculation functions: gabpEval.m
3. Of genetic algorithms for the BP network codec corresponding weights, the threshold function: gadecod.m
Second, with the GA first aim at the weight of BP network, and then direct the training of pure BP mixture of BP algorithm GA-BP
Main program: gabpfault.m It includes the following routines:
1. Network initialization: nninit.m-- given P, T, R, S1, S2
2. Fitness calculation functions: gabpEval.m
3. Of genetic algorithms for the BP network codec corresponding weights, the threshold function: gadecod.m
3, pure BP
Main program: (1) bpfault.m on the MATLAB5.2
(2) bpfault.m the MATLAB6.5 for the subsequently added
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Size: 38912 |
Author: zhanghr |
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Description: How to use the program for your own purposes:
The main thing that needs to be changed in ga.m is the fitness function
and a few parameters. It is now set to minimize a function z=f(x,y)
that is a sum of scaled translated Gaussian distributions with x and y
between 0 and 30 (specified by the elements of HIGHTRAIT and LOWTRAIT).
The function to be optimized must be changed in the section specified
in the program below.
In addition, the following parameters should be modified according
to your needs:- How to use the program for your own purposes:
The main thing that needs to be changed in ga.m is the fitness function
and a few parameters. It is now set to minimize a function z=f(x,y)
that is a sum of scaled translated Gaussian distributions with x and y
between 0 and 30 (specified by the elements of HIGHTRAIT and LOWTRAIT).
The function to be optimized must be changed in the section specified
in the program below.
In addition, the following parameters should be modified according
to your needs:
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Size: 33792 |
Author: ffault |
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Description: 热力学遗传算"~-(therm odynamical genetic algorithms，简称TDGA)借鉴固体退火过程中能量与熵的竞争
模式来协调GA 中“选择压力”和“种群多样性”之间的冲突．然而TDGA 目前极高的计算代价限制了其应用．为了提
高TDGA的计算效率，首先定义一种等级熵(rating—based entropy,J~j称RE)度量方法，它能以较小的计算成本度量种
群中个体适应值的分散程度．然后引入分量热力学替换规则(component thermod)rnamical replacement，简称CTR)，有
效地降低了替换规则的复杂度．同时也证明了CTR规则具有驱动种群自由能近似最速下降的能力．在0．1背包问题
上的实验结果表明，RE 方法和CTR规则在保持TDGA良好的性能与稳定性的同时，极大地提高了其计算效率．-Thermodynamical genetic algorithms(TDGA)simulate the competitive model between energy and
entropy in annealing to harmonize the conflicts between selective pressure and population diversity in GA．But high
computational cost restricts the applications of TDGA．In order to improve the computational efi ciency，a
measurement method of rating—based entropy(RE)is proposed．The RE method can measure the fitness dispersal
with low computational cost．Then a component therm odynamical replacement(CTR)rule is introduced to reduce
the complexity of the replacement，and it is proved that the CTR rule has the approximate steepest descent ability of
the population free energy．Experimental results on 0-1 knapsack problems show that the RE method and the CTR
rule not only maintain the excellent perform ance and stability of TDGA，but also remarkably improve the
computational efi ciency of TDGA．
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Size: 384000 |
Author: 郭事业 |
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Description: 这是求值函数采取仅正面价值的简单的基因算法实施，并且合适的个体是相同的象目标函数的价值
-This is a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
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Size: 220160 |
Author: 李杰 |
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Description: Dynamic linear calibration fitness function
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Size: 2070 |
Author: kimhanxin@163.com |
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