1) multi_reproductions transform
多种群变换
2) multiple farm variation
多种群变异
1.
In view of the slow computational rate and close competition of simple genetic algorithm, we propose an improved genetic algorithm using the multiple farm variation based on the biological theory of intercross advantage, optimal results have been achieved through simulation.
针对基本遗传算法存在的寻优速度较慢、存在“封闭竞争”等问题 ,利用生物界中存在的“杂交”优势原理 ,提出以多种群变异为基础解决以上问题的改进遗传算法 ,并通过仿真取得满意的效
3) the variant semigroups of partial transformation semigroup preserving an equivalence
保等价部分变换半群的变种半群
4) multi-population
多种群
1.
A multi-population differential evolution algorithm and its application in flexible job shop scheduling;
多种群差分进化算法及在柔性车间作业调度中的应用
2.
Task scheduling algorithm based on multi-population and level set;
基于多种群及水平集的任务调度算法
3.
Multi-population Evolution Strategies for Multi-modal Function Optimization;
多模态函数优化的多种群进化策略
5) multiple populations
多种群
1.
Multiple Populations Coevolution and the Application in the Generative Conception Design;
多种群协同进化及其在创新概念设计中的应用
2.
Firstly,this paper put forward new Richards model of multiple populations based on growth model of single population,then gave the biological meaning of related parameters and thought that this model included all relationships effectively among populations (competition,partial-advantage,partial-deleteriousness,mutualism,community,etc).
在单种群生长模型的基础上提出了多种群下的Richards模型,并对相关的参数给出了明确的生物学意义,认为该模型有效地包括了种群的竞争关系,偏利与偏害关系,共生与互惠关系等。
6) multigroup
多种群
1.
To improve the performance of genetic algorithm,greedy algorithm which has more local search ability is imported to genetic algorithm and is well combined with the ideas of simulated annealing and multigroup parallel evolution in this paper.
为了提高遗传算法的性能,论文通过将局部搜索能力较强的贪婪算法引入遗传算法,并且同模拟退火和多种群并行遗传进化思想有机结合起来的方法,提出了一个改进型的算法——多种群退火贪婪混合遗传算法(MultigroupAnnealingGreedyHybridGeneticAlgorithm,简称MAGHGA)。
2.
A parallel version of PSO based on multigroup and migration is proposed.
文章先简要介绍了粒子群算法的标准版本,对比细粒度的粒子群算法的简单并行,引入种群和移民的思想,提出了一种基于多种群的粒子群算法的并行版本。
3.
Then a multigroup parallel genetic algorithm(GA) is used to solve the optimization problem.
针对有色冶金企业原料库存的实际情况 ,建立了一个以资金损耗最小为直接性能指标的原料库存优化模型 ,并提出了一种多种群并行遗传算法对该模型进行优化。
补充资料：种群增长率(见种群出生率)
种群增长率(见种群出生率)
种群增长率见种群出生率。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条