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ga
- 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）。
GAPLL
- 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
GA
- 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
fitnessfunction
- 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
GaPlaygroundCode
- 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
Genetic_Algorithm
- 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 distr
GA
- 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)rnami
GA
- 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
GA_for_clustering
- Genetic algorithms (GAs) have recently been accepted as powerful approaches to solving optimization problems. It is also well-accepted that building block construction (schemata formation and conservation) has a positive influence on GA behavior.
GA.cpp
- 选择:根据遗传算法的收敛定理,赌轮法(蒙特卡罗法) ,进行个体选择。交叉:交叉互换的重要特征是它能产生不同于父体的子体。交叉概率越大,交叉操作的可能性也越大 如果交叉率太低,收敛速度可能降低。单阈值分割由于只有一个参数,所以采用一点交叉,在此设置交叉概率为0.65。变异:变异概率为0.01 。-This is a simple genetic algorithm implementation where the evaluation function takes positive values
ga-vc
- This a simple genetic algorithm implementation where the evaluation function takes positive 这是一个简单的遗传算法实现了评价函数在正面的价值观,只需要一个个体的适应度是相同的目标函数值 -This is a simple genetic algorithm implementation where the evaluation function takes positive values R
GA
- 遗传算法源码，按照适者生存和优胜劣汰的原理，逐代（generation）演化产生出越来越好的近似解，在每一代，根据问题域中个体的适应度（fitness）大小选择（selection）个体，并借助于自然遗传学的遗传算子（genetic operators）进行组合交叉（crossover）和变异（mutation），产生出代表新的解集的种群。这个过程将导致种群像自然进化一样的后生代种群比前代更加适应于环境，末代种群中的最优个体经过解码（decoding），可以作为问题近似最优解。-Genetic
GA
- 1元简单函数优化的遗传算法程序，包括初始化、适应度函数、交叉、变异、选择和主程序以及画图分析。-A simple function of the genetic algorithm optimization procedures, including initialization, fitness function, crossover, mutation, selection and the main program as well as drawing analysis.
matlab-GA-FIR
- 基于遗传算法来实现频率采样FIR滤波器的设计，本程序采用的是谢菲尔德的遗传算法工具箱，有各种函数，在工程里面添加了自己的适应度函数。-Based on genetic algorithm to implement the frequency sampling FIR filters, this procedure USES a genetic algorithm toolbox of Sheffield has a variety of functions, in engineering to
GA
- 用matlab实现的基本遗传算法、顺序选择遗传算法、适值函数标定的遗传算法、大变异遗传算法、自适应遗传算法、双切点交叉遗传算法、多变异位自适应遗传算法-Using matlab realize the basic genetic algorithm sequentially selects genetic algorithm, fitness function of the genetic algorithm calibration, large mutation genetic algorit
GA
- 遗传算法MALAB程序包，包括适应度、计算、选择交叉、变异等子程序-GA MALAB package, including fitness, calculation, select crossover and mutation subroutine
GA
- 利用遗传算法所设计的随机产生地图之推箱子小程序 可参阅内容之Fitness以及基因的变异和交配 -Using genetic algorithms designed randomly generated map of Sokoban program can refer Fitness content and gene mutation and mating
bp-ga-method
- 基于遗传算法的BP神经网络算法(以下简称BP—GA)就是在BP神经网络的学习过程中，将权重和阀值描述为染色体，并选取适宜的适应函数，然后进行GA迭代，直到某种意义上的收敛．-GA BP neural network algorithm (hereinafter referred to as BP-GA) is in the learning process BP neural network, the weights and thresholds described as chromosomes
using-GA-in-antenna-array
- 提出了整数编码，动态调整交叉概率、变异概率，并将适应度函数设为最大相对旁瓣电平,一种改进遗传算法的优化方法来实现直线稀疏阵列的设计-Proposed integer coding, dynamic adjustment of crossover probability, mutation probability, and the fitness function is set to the maximum relative sidelobe level, an improved genetic
GA
- This program has evolved (hopefully to achieve a higher fitness!) over time using programming ideas several persons including LaMoyne Porter, Will Lennon, Jonathan Cook, and Jim Gremling.-This program has evolved (hopefully to achieve a higher