搜索资源列表
yichuansuanfa
- 遗传算法，通过选择、交叉、变异求取函数最优解c程序简单实现，并拥有最优保存思想。-Genetic algorithm, selection, crossover and mutation to strike a function of the optimal solution c procedures are simple to achieve, and has saved the best idea.
hanshu
- MATLAB,通过遗传算法实现函数的最值问题，包含选择，交叉以及变异操作-MATLAB, through the genetic algorithm function, the most value problems, including selection, crossover and mutation operations
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
NSGA-II
- his function is utilized to produce offsprings from parent chromosomes. The genetic operators corssover and mutation which are carried out with slight modifications from the original design. For more information read the document enclosed. -
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
matlab
- mutation operation in genetic algorithms
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
Genetic_fuzzy_control
- A TSK type fuzzy controller designed by GA-by tournament.initial mutation probability
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.
Laplace(ImageSharpening)
- 图像锐化 Laplace算子 拉普拉斯算法不检测均匀的亮度变化，而是检测变化率的变化率，相当于二阶微分。计算出的图像更加突出亮度值突变的位置。-Image Sharpening Laplace operator Laplace algorithm does not detect uniform brightness changes, but the detection rate of change of the rate of change is equivalent to second-ord
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.
TSP
- 提出一种改进的禁忌搜索算法来求解背包问题。该算法基于禁忌搜索技术,并采用I&D策略,同时设计了两种针对局 部最优解的变异算子。改进后的算法能有效地弥补标准禁忌算法对初始解依赖的缺陷,同时也避免了搜索停滞的现象。通过对具 体实例和随机问题的测试,表明改进后的禁忌搜索算法有更好的性能。 关-An improved tabu search algorithm to solve knapsack problem. The algorithm is based on tabu
tspmatlab
- 提出一种改进的禁忌搜索算法来求解背包问题。该算法基于禁忌搜索技术,并采用I&D策略,同时设计了两种针对局 部最优解的变异算子。改进后的算法能有效地弥补标准禁忌算法对初始解依赖的缺陷,同时也避免了搜索停滞的现象。通过对具 体实例和随机问题的测试,表明改进后的禁忌搜索算法有更好的性能。 关-An improved tabu search algorithm to solve knapsack problem. The algorithm is based on tabu
sfsf
- 提出一种改进的禁忌搜索算法来求解背包问题。该算法基于禁忌搜索技术,并采用I&D策略,同时设计了两种针对局 部最优解的变异算子。改进后的算法能有效地弥补标准禁忌算法对初始解依赖的缺陷,同时也避免了搜索停滞的现象。通过对具 体实例和随机问题的测试,表明改进后的禁忌搜索算法有更好的性能。 关-An improved tabu search algorithm to solve knapsack problem. The algorithm is based on tabu
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
cp321123
- 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交
GAreal_tourney
- ntroduction to Stochastic Search and Optimization, 2003 This program runs a GA with real-number coding. Elitism is used and the mutation operator is simply the addition of a Gaussian random vector to the non-elite elements. The user