搜索资源列表
yizhgaijindeyic
- 一种改进的遗传算法程序,二进制编码的交叉算子,FloatExample和BinaryExample分别采用浮点编码和二进制编码方法-An improved genetic algorithm process, the binary-coded crossover operator, FloatExample and BinaryExample respectively floating point coding and binary coding method
RGA
- 这是一个采用了UNDX-MMG模型的实数遗传算法。有5个测试函数。-the code is for real coded genetic algorithm using UNDX crossover and MMG model. it is tested using five bench mark function
crccontrol
- The following program is written to simulate a randomly generated message under BSC(p) channel. It examines whether or not the generator polynomial to be strong enough to be used at a given packet size and crossover probability p.
yichuansuanfa
- fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-fga.m the main program for the genetic algorithm binary Gray encoding, roulette wheel method based on non-linear ranking selection, uniform crossover and mutation operations, but al
GA-PNestimation
- 用遗传算法估计伪随机码（PN序列），包含初始化，选择，交叉，变异-Is estimated by using genetic algorithms pseudo-random code (PN sequence), including initialization, selection, crossover and mutation
genetic_algorithms
- 遗传算法，使用浮点编码，使用自适应的交叉和变异因子-Genetic algorithm, the use of floating-point encoding, the use of adaptive crossover and mutation factor
the_use_of_ga
- 给出了遗传算法的基本参数设置要求，包括交叉、变异、选择-Given the basic parameters of genetic algorithm to set demands, including the crossover, mutation, selection, etc.
KnapsackProblem
- 基本遗传算法带最优保存思想的背包问题，其中，目标值那段代码使用的是惩罚函数法，选择是概率选择，交叉是双点随机交叉，变异是概率变异-The basic genetic algorithm with elitist thinking knapsack problem, which is a target that part of the code using penalty function method, choice is the probability of selection, crosso
VHDL
- 分频跑马灯数码管示范代码能实现分频跑马灯数码管示范-Crossover Marquee digital control Model Code
genetic
- matlab遗传算法编码 采用基本遗传算法 同时加入刘海交叉法对算法进行改进 解决TSP问题-matlab genetic algorithm coding the basic genetic algorithm using crossover method Liu also added to improve the algorithm to solve TSP problems
GeneticAlgorithms
- 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, to achieve the selection operation, crossover operation and mutation operation, through the completion of the fitness function the choice of populations and convergence.
pso
- 粒子群优化(Particle Swarm Optimization - PSO) 算法是一种新兴的有潜力的进化算法( Evolutionary Algorithm - EA) .PSO 算法,和遗传算法相似,它也是从随机解出发,通过迭代寻找最优解,它也是通过适应度来评价解的品质. 但是它比遗传算法规则更为简单,它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作. 它通过追随当前搜索到的最优值来寻找全局最优。-pso
GAfun
- 遗传算法在函数中的应用，它包括编码、交叉、变异、选择等详细的编程，对学习遗传算法很有帮助。-Genetic Algorithms in a function application, which includes coding, crossover and mutation, select details such as programming, genetic algorithm is useful for learning.
Matlabcode
- 遗传算法，可实现多种选择和交叉、变异算法-Genetic algorithm, can realize a variety of selection and crossover and mutation algorithm
Guotao-algorithmic
- 线性非凸多父体杂交算子求解TSP问题。算法将搜索空间看成是一个全空间Ω，种群中的个体可以看作Ω中的一组向量。种群中的若干个向量构成一组基向量，它们可以张成Ω的一个子空间，这些向量随机性组合能相对均匀地搜索这一部分子空间。-Linear non-convex multi-parent crossover operator for solving TSP body problem. Algorithm search space as a full-space Ω, the individual po
yichuansuanfa_jixieshou
- 提出一种改进的遗传算法用于求解机械手运动学逆问题. 该算法采用实数编码, 其交叉概率和变异 概率根据解的适应度函数值自适应调整. 计算机仿真结果显示, 该算法较简单遗传算法(SGA) 求解精度高, 收敛速度快且稳定性能好.-An improved genetic algorithm for solving the inverse problem of manipulator kinematics. The algorithm uses real number encoding, the
GA
- 遗传算法求解函数最大值 设计的种群规模，采用的选择算子，交叉概率，变异概率，进化代数和最优解-Design of population size, using the selection operator, crossover probability, mutation probability, evolutionary algebra and the optimal solution
GA
- C++ implementation of a Genetic algorithm (GA). A population of binary chromosomes is generated randomly to attempt to solve the Weighted MAX SAT Problem. Parameters of crossover, mutation, population size can be controlled via macros in code.There a
Genetic
- 遗传算法中，初始化，编码，解码，选择，交叉，变异，倒位的源程序-Genetic algorithms, initialization, encoding, decoding, selection, crossover, mutation, inversion of the source code
Ga
- 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作. -Binary Gray encoding, roulette wheel method based on non-linear ranking selection, uniform crossover and mutation operations, but also introduces the inversion operation.