| 1. | Surface ship target recognition research based on sga
应用基本遗传算法进行水面舰船目标识别研究 |
| 2. | This paper introduces the basic ga and its development , and provide an arithmetic to the diagnosis equations with ga
首先讨论了基本遗传算法及其改进算法,结合故障诊断方程的特点,提出了基于ga算法的故障诊断方程求解方法。 |
| 3. | Chaos optimization search operation is introduced to simple genetic algorithm operation for mending the defect that simple genetic algorithm is premature easily
在基本遗传操作中引入了混沌优化搜索操作,克服了基本遗传算法容易“早熟”的缺陷。 |
| 4. | Compared with the computational result of traditional ga , it shows that the searching efficiency of ga can be improved remarkably and the fluctuation of random searching can be reduced by recognizing building block
与基本遗传算法的计算结果对比分析表明，所提算法可显著提高遗传算法的搜索效率，减小遗传算法随机搜索的波动性。 |
| 5. | In this thesis , following improving of the simple genetic algorithm , the improved genetic algorithm is used to solve the problem of logistics distribution center location , getting the resolution of the location model
本文在改进基本遗传算法基础上，然后利用该改进的遗传算法对物流配送中心选址问题进行优化求解，并结合实际模型，提出了“混合并行编码”的编码思想。 |
| 6. | In the fourth chapter , on the basis of definition of the mathematic model of pumping station in dispatching , and considering the task of dispatching , the author choose genetic algorithm to settle the problem
第四章中，首先建立系统的数学模型，针对优化调度的任务要求，选用了遗传算法。而后，详细讨论了基本遗传算法的实现，分析了基本遗传算法的缺陷并给出改进的方法。 |
| 7. | The basal genetic algorithm bears only cross - over operator and mutation operator which makes it has the weak point in the search ability , thus the prematurity often occur and the result often converge to the vicinity of the real optimal point
基本遗传算法仅有交叉算子和变异算子，因而局部搜索能力不强，容易出现种群早熟，进化结束时往往收敛到最优点附近而达不到全局最优点。 |
| 8. | Because no literature discusses that how to confirm the appropriate population size , this paper discusses how the population size affects ga ' s optimal course based on three examples . this paper confirms the better population size
由于没有这方面相关的文献资料，本文还针对群体规模对基本遗传算法的优化计算影响的问题结合三个算例进行了尝试性的对比计算，确定了较佳的群体规模。 |
| 9. | This paper starts with the basic theory of genetic algorithms . then , some modification methods are advanced and some convergence proofs made , aiming at the problem that the probability of simple genetic algorithm ( sga ) converged to optimal solution is less than 1
本文从遗传算法的基本理论入手，针对基本遗传算法( sga )不以概率1收敛于最优解的问题，提出了一些改进方法并对其收敛性进行证明。 |
| 10. | After the characters , development , application and the foundational theory of genetic algorithm being introduced , the simple genetic algorithm is improved on in this thesis aiming at its application limitation . the improving work is as follows
在对遗传算法的特点、发展过程、应用领域以及其理论基础介绍之后，本文针对基本遗传算法的应用存在的局限性，对其进行改进，主要包括以下几方面的工作。 |
