搜索资源列表
NSGA-IIMATLAB
- NSGA-II 效果很好 很多人里面的交叉变异都有误解 对刚接触多目标的人很有用处-NSGA-II works well inside the crossover and mutation, many people have a misunderstanding of the goals of people new to many useful
ss
- 遗传算法解决背包问题，其中包括算法编码初始化，交叉，变异和惩罚策略，很好的解决了背包问题-Genetic algorithm to solve knapsack problem, including the coding algorithm initialization, crossover, mutation and punishment strategy, a good solution to the knapsack problem
Genetic
- 用遗传算法解决旅行商问题，种群规模为2000，变异方法是将第二位上的基因放到第一位之前，适应函数为1除以路径长度8次方 ，算法迭代125次。 -Solve the using genetic algorithms, population size is 2000, the second mutation is the gene into the first before the fitness function as a path length divided by the 8
ga
- 包括遗传算法的各种运算（交叉，变异）的实例源程序代码-Various operations, including genetic algorithms (crossover and mutation) of the instance of the source code
SGAPublic
- 本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值啦） 并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉； 单点变异，浮点变异；-The SGA can optimize the variables, find minimum, maximum (when the function is also seeking the minimum reciprocal friends) and support for floating-
ModelGenerationforanIntrusionDetection
- 记载每一代的进化过程中保留多个最优个体不参加交叉，变异等遗传运算，而直接将他们复制到下一代群体中。-Documented the evolution of each generation to keep more than the best individual does not participate in crossover and mutation genetic operators, and direct them to the next generation of copy group
RAGA
- 实数编码遗传算法，采用自适应性变异，有范例，效果良好。-GA base on float,which use the auto-fitness mutation.
ga01
- 遺傳演算法是計算數學中用於解決最優化的搜索演算法，是進化演算法的一種。進化演算法最初是借鑒了進化生物學中的一些現象而發展起來的，這些現象包括遺傳、突變、自然選擇以及雜交等。-Genetic algorithm is used to solve mathematical calculations to optimize the search algorithm is a kind of evolutionary algorithms. Evolutionary algorithm was orig
simple_evolution_algorithm
- 一个简单的进化算法实现，包括选择、交叉、变异-A simple evolutionary algorithm, including selection, crossover, mutation
SimpleGA_1
- 实现简单的进化算法，实数编码，轮盘赌选择方式，单点交叉，均匀变异。-Simple evolutionary algorithms, real coding, roulette wheel selection method, single-point crossover, uniform mutation.
final
- 遗传 算法 ，基于matlab实现，通过选择、交叉、变异，经过多次的迭代从而找到最优个体-Genetic algorithm, based on matlab implementation, through selection, crossover, mutation, after several iterations to find the best individual
yichuansuanfahanshuyouhua
- 对遗传算法思想的理解和认识,对一个函数求其最小值，采用遗传算法的交叉，变异等特征进行优化求解-Genetic algorithms for understanding and awareness of thoughts, seeking the minimum of a function, using genetic algorithm crossover and mutation characteristics of the optimal solution
GA.ZIP
- 这是另一个采用FORTRAN语言编写的遗传算法的源程序，包括很多交叉和变异模型。-This is another use of FORTRAN language source of genetic algorithms, including a lot of crossover and mutation model.
MTT
- 气候分析中检验突变程序,滑动t检验法的Fortran程序-Climate Analysis mutation test procedures for sliding t test method of Fortran programs
backpack
- 用C#来实现遗传算法中的背包问题。其中只有变异过程。-Using C# to implement the genetic algorithm knapsack problem. Only mutation process.
AdapGA
- Srinvivas提出的自适应遗传算法，交叉概率和变异概率随适应度自动改变-Srinvivas proposed genetic algorithm, crossover probability and mutation probability automatically change with fitness
GMGA
- 大变异遗传算法以一个远大于通常的变异概率执行一次编译操作，从而使整体总群脱离“早熟”。-Genetic algorithm with a large variation is much larger than the mutation probability is usually compiled once operation, so that the overall total group from the " premature."
MATLAB
- 这是关于用MATLAB实现遗传算法程序的原理介绍，包括也选择、交叉、变异等算子的程序。-This is about using MATLAB genetic algorithm program to achieve the principle of descr iption, including also selection, crossover and mutation operators of the program.
PP
- 摘 要：0IE 问题是一个典型的优化组合问题，现在有很多解决的方法。本文针对遗传算法求解0IE 问题进行了研究，对 选择、交叉和变异算子进行了算法设计，最后在_ C- N软件上进行编程实现。结果表明，遗传算法在求解0IE 问题时具有 结果准确、收敛速度快等特点。 关键词：遗传算法；0IE 问题；最优化问题；_ C- N 软-Abstract: 0IE problem is a typical combinatorial
SJ
- UbW 遗传算法是近几年发展起来的一种崭新的全局优化算法， 针对 问题，编码规则通常是取 进制编码 ，即每个基 0IE B 它借用了生物遗传学的观点，通过自然选择、遗传和变异等作 因仅从 到 的整数里面取一个值，每个个体的长度为 ，为 -UbW genetic algorithm is developed in recent years, a new global optimization algorithm, solve the probl