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RealGA(TwoVaribles)
- 此代码是实数编码遗传算法的Matlab例程。用了精英保留策略选择和轮盘赌选择法，叉操作采用中间重组方式。-This code is a real-coded genetic algorithm Matlab routines. Retention policies with the elite selection and roulette wheel selection method, fork operation mode using the middle of restructuring.
GAdisplacement
- 用遗传算法对光纤位移传感器的实验数据进行拟合，使用精英保留策略提高算法性能-Genetic algorithm with fiber-optic displacement sensor to fit the experimental data, the use of elite strategies to improve retention of algorithm performance
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
final
- 使用遺傳演算法來解決一個多項是問題 然是使用單點突變以及單點交配 使用二進位編碼以及精英輪盤法-The use of genetic algorithms to solve the problem then is a number of single point mutations and single point crossover using binary coding and the elite roulette method
M-Elite-Coevolutionary-Algorithm
- 一种新的遗传算法介绍，该文章提出了一种新的遗传算法，基于M个精英选择组成M个团体，精英与精英之间进行协作，精英与普通个体之间进行引导-a new genetic algorithm with M elites using a coroperation and leading strategy
GA0-1
- 0-1背包问题遗传算法，包含精英选择和非精英选择-0-1 knapsack problem algorithms, comprising elite and non-elite choose
douelitePSO3D
- 基于精英粒子群算法的双机器人路径规划，白车车身焊接，不考虑避障-Dual path planning algorithm based on particle swarm elite, white car body welding, without considering obstacle avoidance
遗传算法
- 遗传算法函数——每一代都将精英保留到下一代(Genetic algorithm - retention of elite)
nsga-2
- 快速非支配排序算法，引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度;采用拥挤度和拥挤度比较算子。(The fast non dominated sorting algorithm introduces the elite strategy to ensure that some excellent individual individuals will not be discarded during the evolution process, thus i
生产调度
- 多工序多设备的生产车间调度问题，采用的是精英保留策略的遗传算法(A genetic algorithm based on elite reservation strategy is adopted for multi-process and multi-equipment job shop scheduling problem.)
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性