| 1. | Implicit parallelism and convergence are analyzed . in chapter 3 , improvements on ga are investigated
第三章提出了对遗传算法的改进，是本文理论算法研究的重要部分。 |
| 2. | And because of its independence , global optimization and implicit parallelism , ga is developed and applied by more and more people
由于其具有不依赖于问题模型的特性、全局最优性、隐含并行性等特点，正越来越激起人们研究与应用的兴趣。 |
| 3. | Ga is a computational models of the human evolution , with implicit parallelism and capacity of using effectively global information
遗传算法( ga )是一种通过模拟自然进化过程搜索最优解的方法，其显著特点是隐含并行性和对全局信息的有效利用能力。 |
| 4. | As one of global optimal algorithm , and because of its simply application , global optimization and implicit parallelism . in recent years genetic algorithms have been used in the field of scheduling more and more generally
它作为一种新的全局优化搜索算法，以其实现简单、通用、鲁棒性强、适于并行处理等显著特点，广泛应用各种优化问题。 |
| 5. | The paper discusses the basic theory of genetic algorithms including schemate theorem , building block hypothesis , implicit parallelism , the analysis of astringency and so on , as the theoretical base of application
在对遗传算法的阐述中，讨论了遗传算法的基本原理，包括模式定理、积木块假设、隐含并行性和遗传算法的收敛性分析等，作为后面遗传算法应用的理论依据。 |
| 6. | Ga poses implicit parallelism and is suitable for implementation on large scale parallel computers . dividing the whole population into sub - populations and coarse - grained island model of exchanging information among sub - populations are the most direct parallel method
Gas具有天然的并行性，非常适合于在大规模并行计算机上实现，把串行gas中的单一群体分成多个子群体，各子群体之间相互交换信息的粗粒度并行是将gas并行化的最直接方式。 |
| 7. | Aiming at the implicit parallelism of ga and the characteristic of dps system , we study the parallelization of the former two algorithms . the basic idea is to put forward an agents - based model of parallel coalition formation algorithm on the basis of coarse - grained parallel genetic algorithm
由于遗传算法的隐并行性以及dps系统的特性，我们在文中对上述两种算法的并行化做了研究，基本的思想是在粗粒度并行遗传算法模型的基础上提出一种基于agents的并行联盟形成算法模型。 |
