搜索资源列表
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
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.
genetic-algorithm-vc
- 用VC实现的遗传算法的完整工程文件。有转轮法，交叉和变异。-VC genetic algorithm implemented with the full project file. There wheel method, crossover and mutation.
GA3
- 遗传算法--函数最优化任意随机交叉父体。在遗传算法中引入任意随即交叉概念，增加个体多样性。-Genetic algorithms- function is to optimize the parent body of arbitrary random crossover. The introduction of any genetic algorithm is then cross-concept, increase the diversity of individuals.
360139
- In this paper, an attractive approach for teaching genetic algorithm (GA) is presented. This approach is based primarily on using MATLAB in implementing the genetic operators: crossover, mutation and selection
SGA
- 基本遗传算法代码实现，选择交叉变异 对于初学遗传算法的同志很有帮助，希望大家一起提高进步-Code to achieve the basic genetic algorithm, select the crossover and mutation genetic algorithm for beginners comrades helpful, hope all of us to improve progress
tsp
- 该程序解决10个城市的货郎担问题（TSP），主要使用简单的遗传算法实现。该过程中包括：编码、解码，选择，交叉，变异等！-The program to solve the traveling salesman problem of 10 cities (TSP), the use of simple genetic algorithm. The process includes: encoding, decoding, selection, crossover and mutation!
SGA_C
- 基本遗传算法的实现，包括简单的交叉，变异操作-Basic genetic algorithm, including the simple crossover and mutation operators
GA-1
- 遗传算法求解函数极值问题。完整实现了遗传算法的选择/交叉/变异等功能，可设定交叉和变异概率。C++语言，输出为文件形式-Genetic Algorithm for function extremum problem. Full realization of the genetic algorithm selection/crossover/mutation and other functions, can be set crossover and mutation probability. C+