Description: 基于改进后的遗传算法 交叉、变异操作后，在windows平台下用C语言实现求解TSP问题-Based on the improved genetic algorithm crossover and mutation operation, in windows platform using C language for solving TSP problems Platform: |
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Author:lc |
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Description: 旅行商问题（TSP）是一个经典的优化组合问题，本个案列采用遗传算法来求解TSP问题，进行了选择、交叉、变异算子的设计，并通过MATLAB对算法进行了实现，附有详细的说明和代码。,The traveling salesman problem (TSP) is a classic combination optimization problem, in this case the column using a genetic algorithm to solve TSP problem selection, crossover and mutation operator design and realization by MATLAB algorithm, with detailed description and code. Platform: |
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Author:ZHENG |
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Description: 旅行商问题(TSP)是典型的NP完全问题，遗传算法是求解NP完全问题的一种常用方法。旅行商问题(TSP)的蚁群算法实现算法。在MATLAB中用遗传算法施行对TSP问题进行了求解，进行了选择、交叉和变异算子进行了算法设计。MATLAB源代码。-Traveling Salesman Problem (TSP) is a typical NP-complete problem, genetic algorithm for solving NP-complete problems, a common method. Traveling Salesman Problem (TSP) ant colony algorithm. Implemented in MATLAB using genetic algorithms to solve the TSP problem, a selection, crossover and mutation operators of the algorithm design. MATLAB source code. Platform: |
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Author:申悦 |
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Description: 遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法
遗传算法的基本运算过程如下：
a)初始化：设置进化代数计数器t=0，设置最大进化代数T，随机生成M个个体作为初始群体P(0)。
b)个体评价：计算群体P(t)中各个个体的适应度。
c)选择运算:将选择算子作用于群体。选择的目的是把优化的个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代。选择操作是建立在群体中个体的适应度评估基础上的。
d)交叉运算：将交叉算子作用于群体。所谓交叉是指把两个父代个体的部分结构加以替换重组而生成新个体的操作。遗传算法中起核心作用的就是交叉算子。-Genetic algorithm (Genetic Algorithm) is a computational model of biological evolution of natural selection and genetic mechanism of biological evolution of the simulation of Darwin, is a kind of method to search the optimal solution by simulating natural evolutionary process
The basic operation process of genetic algorithm as follows:
A initialization settings): the evolution algebra counter t=0, set the maximum evolution algebra T, randomly generated M individuals as the initial population of P (0).
B) individual uation: Calculation of group P (T) in the fitness of each individual.
C) selecting operation: the selection operator acting on the group. The choice of the purpose is to direct individual genetic optimization to the next generation, or by paired crossover generates new individuals and then transmitted to the next generation. The choice of operation is based on individual fitness uation based on.
D) crossover: crossover operator acting on the group. The so-called cross Platform: |
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Author:ahu_gj |
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Description: 应用遗传算法解决旅行商（TSP）问题，算法中的染色体交叉过程改进了基本遗传算法的交叉办法。改程序可以求解30个城市的旅行商问题，也可以稍作修改继续扩展为更多城市的旅行商问题。-Applying the genetic algorithm to solve the traveling salesman (TSP) problem, the algorithm improves the process of chromosomal crossover compared to traditional crossover approach. It can solve the traveling salesman problem in 30 cities and also can be slightly modified to continue to expand to more cities in the traveling salesman problem.
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Author:马骏 |
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Description: 本资源是Mgasa算法解决TSP问题的Matlab代码，资源中包括mgasa_main(Mgasa算法解决TSP问题代码)，mgasa_fitness(适应度求取函数代码)，mgasa_annealing(Mgasa算法中模拟退火代码)，mgasa_select(遗传算法中选择函数代码)，mgasa_crossover(遗传算法中染色体交叉互换函数代码)，mgasa_mutation(遗传算法中基因突变函数代码)，mgasa_change(Mgasa算法中选择过程代码)。同时代码中有Location矩阵，其中30个坐标作为TSP问题的例子。-This resource is Mgasa algorithm to solve the TSP problem matlab code, resources including the mgasa_main, the algorithm Mgasa solutions TSP code, mgasa_fitness (adaptation degree to obtain the function code), mgasa_annealing (Mgasa algorithm simulated annealing of code), mgasa_ (genetic algorithm selection function code), mgasa_crossover (genetic algorithm crossover swap function code), mgasa_mutation(genetic algorithm in gene mutation function code), mgasa_change(algorithm Mgasa selects the process of code). At the same time the code in the Location matrix, where 30 coordinates as an example of the TSP problem.
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Author:lfr |
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Description: 基于遗传算法的TSP问题实现，实例有30个城市的距离路径数据，采取两种交叉算子实现。-Based on genetic algorithm for the realization of TSP, examples of 30 cities in the distance path data, using two kinds of crossover operator. Platform: |
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Author:冯木木 |
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Description: 混合粒子群算法摒弃了传统粒子群算法中的通过跟踪极值来更新粒子位置的方法，而是引入了遗传算法中的交叉和变异操作，通过粒子同个体极值和群体极值的交叉以及粒子自身变异的方式来搜索最优解。(Hybrid particle swarm algorithm instead of the traditional particle swarm algorithm in the method to update the position of the particle by tracking the maximum, but the introduction of crossover and mutation in genetic algorithm, the particle swarm extremum with individual extremum and cross and variation of the particle itself to search the optimal solution.) Platform: |
Size: 13312 |
Author:胡萝卜须
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Search - crossover in Genetic algorithm in TSP