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【AI-NN-PR】遗传算法优化的BP神经网络 DL : 1: 遗传算法优化BP 神经网络是用遗传算法来优化BP 神经网络的初始权值和阔值，使优化后的BP 神经网络能够更好地预测函数输出。遗传算法优化BP 神经网络的要素包括种群初始化、造应度函数、选择操作、交叉操作和变异操作。(Genetic algorithm optimizes BP neural network by using genetic algorithm to optimize the initial weight and broad value of BP neural network, so that the optimized BP neural network can better predict function output. The elements of genetic algorithm optimizing BP neural network include population initialization, response function, selection operation, crossover operation and mutation operation.)
Update : 2024-05-04 Size : 54272 Publisher : 孺子可教
【matlab】遗传算法 DL : 0: 遗传算法以一种群体中的所有个体为对象，并利用随机化技术指导对一个被编码的参数空间进行高效搜索。其中，选择、交叉和变异构成了遗传算法的遗传操作；参数编码、初始群体的设定、适应度函数的设计、遗传操作设计、控制参数设定五个要素组成了遗传算法的核心内容。(Genetic algorithm takes all individuals in a group as objects, and uses randomized technology to guide efficient search of a coded parameter space. Among them, selection, crossover and mutation constitute the genetic operation of genetic algorithm. Parameter coding, initial population setting, fitness function design, genetic operation design, and control parameter setting constitute five elements of the core content of genetic algorithm.)
Update : 2024-05-04 Size : 4096 Publisher : YZ大龙猫
【Other】 code DL : 0: 基于蚁群算法的 TSP 求解，分别采用蚁群算法和蚁群算法-粒子群混合算法进行优化求解，使用不同的交叉和变异适应度函数更新粒子，从而实现 TSP问题的优化求解，更加逼近实际问题。(Based on the TSP solution of ant colony algorithm, ant colony algorithm and hybrid algorithm of ant colony algorithm particle swarm optimization are used to solve the TSP, and different fitness functions of crossover and mutation are used to update the particles, so as to achieve the optimal solution of TSP, which is closer to the actual problem.)
Update : 2024-05-04 Size : 5120 Publisher : Fantasy1017
【AI-NN-PR】 neural-network-genetic-algorithm-master DL : 0: 为了自动学习CNN的深度网络结构，网络结构的数量随着网络中间层数量的增加呈指数增长，这启发我们使用遗传算法有效地遍历这个大的搜索空间。我们首先提出一种编码方法，将每个网络结构表示为一个固定长度的二进制字符串，然后通过生成一组随机个体来初始化遗传算法。在每一代中，我们定义标准的遗传操作(如选择、突变和交叉)来消除弱势个体并产生更具竞争力的个体。(In order to automatically learn the deep network structure of CNN, the number of network structure increases exponentially with the increase of the number of network middle layer, which inspires us to use genetic algorithm to effectively traverse this large search space.We first propose a coding method to represent each network structure as a binary string of fixed length, and then initialize the genetic algorithm by generating a set of random individuals.In each generation, we define standard genetic operations (such as selection, mutation, and crossover) to eliminate vulnerable individuals and produce more competitive ones.)
Update : 2024-05-04 Size : 8192 Publisher : 崔宁敏
【】 CEC 2017 bound constrained benchmarks DL : 6: CEC2017前几名的MATLAB算法实现有EBOwithCMAR; jSO; LSHADE_SPACMA; LSHADE-cnEpSin 各种参数都可以调整，包括种群数量、F因子、变异率、交叉率等(The realization of MATLAB algorithm for the top few of cec217. There are ebowithcmar; JSO; lshade_spacma; lshade cnepsin. Various parameters can be adjusted, including population number, F factor, mutation rate, crossover rate, etc.)
Update : 2024-05-04 Size : 25936896 Publisher : GHao
【matlab】免疫算法求解配送中心选址问题matlab代码 DL : 0: 免疫算法求解配送中心选址问题，配送中心向需求点配送货物是供应链中的重要部分.本文以成本最低为目标函数,把距离上限加入到惩罚机制,并根据抗体和抗原之间的亲和力设计自适应交叉和变异概率,把自适应的免疫算法应用到配送中心模型中进行求解,最后通过仿真实验对比验证了算法用在配送中心选址上有较好的效果.(Immune Algorithm is used to solve the location problem of Distribution Center, which is an important part of supply chain. This paper takes the lowest cost as the objective function, adds the upper distance limit to the penalty mechanism, and designs the adaptive crossover and mutation probability according to the affinity between antibody and Antigen, the adaptive immune algorithm is applied to the distribution center model to solve the problem. Finally, the simulation results show that the algorithm is effective in the location of Distribution Center.)
Update : 2024-05-04 Size : 31744 Publisher : 代码大小姐
【AI-NN-PR】 GA-PSO DL : 1: PSO算法计算函数极值时，常常出现早熟现象，导致求解函数极值存在较大的偏差，然而遗传算法对于函数寻优采用选择、交叉和变异算子操作，直接以目标函数作为搜索信息，以一种概率的方式来进行，因此增强了粒子群优化算法的全局寻优能力，加快了算法的进化速度，提高了收敛精度。(When PSO algorithm calculates function extremum, it often appears premature phenomenon, which leads to large deviation in solving function extremum. However, genetic algorithm uses selection, crossover and mutation operators for function optimization, and directly takes the objective function as the search information in a probabilistic way. Therefore, it enhances the global optimization ability of particle swarm optimization algorithm and speeds up the progress of the algorithm The convergence accuracy is improved.)
Update : 2024-05-04 Size : 10240 Publisher : Shuai Wang

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