| 1. | The experiments show ngaaps has prominently better convergent performance than simple genetic algorithm
实验数据表明，该算法具有比简单遗传算法好的收敛性能。 |
| 2. | In the searching computation the new offspring is obtained according to the whole information of its parents
针对简单遗传算法容易陷入局部最优值的缺陷，借鉴和声算法产生新解的方式来产生遗传算法中的子代个体。 |
| 3. | About genetic algorithms , its basic principle , operation , schema theorem , and the realization in the computer are discussed firstly
对于遗传算法( ga ) ，本文讨论了它的基本原理、操作步骤、模式理论及ga的计算机实现问题，以及简单遗传算法的几种改进措施。 |
| 4. | Because simple genetic algorithm is good at the global search and short of the local search , searching the best optimized solution need to consume more time
由于简单遗传算法仅擅长全局搜索，而局部搜索能力不足，要达到真正的最优解则要花费相当长的时间。 |
| 5. | According to the results of the experiments on simulated data , prga gives better results in curve fitting compared with simple ga and traditional numerical iterative method
实验证明，该方法对于曲线拟合问题能取得优于简单遗传算法和传统数值迭代方法的结果。 |
| 6. | Sga has defects of slow convergence and being prone to immature convergence . in order to eliminate the defects , an improved ga is proposed in this thesis
简单遗传算法( sga )存在着收敛速度慢、易“早熟”等缺陷，针对这些缺陷，本文设计出改进遗传算法（ iga ） 。 |
| 7. | The co - evolution model and algorithm of the collaborated optimization design for the scheme design and detail design is constructed based on the simple genetic algorithm
本文以简单遗传算法为基础，建立了实现基坑支护方案与细部协同优化设计的协同演化模型与算法。 |
| 8. | In the first part of this thesis , to cope with the low searching efficiency and premature of simple genetic algorithm ( sga ) , a kind of improved genetic algorithm ( iga ) is presented
本文首先针对简单遗传算法( sga )中存在的收敛速度慢、易陷入局部极小点(即早熟)等缺点，对其进行了改进。 |
| 9. | New genetic algorithm with adaptive population size ( ngaaps ) is proposed to overcome premature convergence and slow convergent speed in the later evolution process of simple genetic algorithm
摘要针对简单遗传算法存在早收敛和在进化后期搜索效率较低的缺点，提出了一种新的种群数自适应遗传算法。 |
| 10. | A new genetic algorithm with adaptive population size ( ngaaps ) is proposed to overcome premature convergence and slow convergent speed in the later evolution process of simple genetic algorithm
摘要针对简单遗传算法存在早收敛和在进化后期搜索效率较低的缺点，提出了一种新的种群数自适应遗传算法。 |
