Description: matlab编写的遗传算法优化程序，采用随机配对交叉，多点交叉，两点交叉，编译对目标函数进行优化-matlab genetic algorithm to optimize the preparation procedures, using a random cross-matching, multi-point crossover, two cross-cutting, the compiler of the objective function to optimize Platform: |
Size: 1024 |
Author:夏雨泽 |
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Description: 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, crossover is a two-point random crossover and mutation is the probability of mutation Platform: |
Size: 2048 |
Author:田文杰 |
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Description: 遗传算法是一种进化算法，应用广泛，此文件包含基本遗传算法，顺序选择遗传算法，适值函数标定的遗传算法，大变异遗传算法，自适应遗传算法，双切点交叉遗传算法，多变异位自适应遗传算法matlab源代码，供大家参考-Genetic algorithm is an evolutionary algorithm, widely used, this file contains the basic genetic algorithm, genetic algorithm selection order coincided with the genetic algorithm calibration function, a large variation of genetic algorithm, adaptive genetic algorithm, two-cut point crossover genetic algorithm, variable ectopic Adaptive genetic algorithm matlab source code for your reference
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Size: 6144 |
Author:熊杰 |
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Description: 基于matlab平台上实现的遗传算法交叉算子操作，实现的是对自然数编码的两点交叉。代码无错，可以直接运行。-Based on matlab platform to achieve crossover genetic algorithm operations, the realization of the two-point crossover natural number coding. Code is error-free, can be directly run. Platform: |
Size: 1024 |
Author:Borney Liu |
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Description: 测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St\ FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.) Platform: |
Size: 14336 |
Author:朱朱521 |
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Search - two point crossover