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On the basis of Simple Genetic Algorithms (SGA), a series of improvements have been put forward. 在传统遗传算法的基础上，提出了一系列的改进策略。
First, brief introduction of GA is given and pseudo code description of simple genetic algorithm (SGA) is presented. 首先简要介绍遗传算法并给出了基本遗传算法(SGA)的伪代码描述；
This paper presents a method that improves the simple genetic algorithms (SGA) with bipolar compressed functional fitness scaling and selection operator based on crowding model. 运用双极性压缩函数适应度定标和基于排挤方法的选择算子改进标准遗传算法（SGA），使其成为简单通用、快速收敛的并行全局搜索算法。
In the end, a benchmark example is included that demonstrates the features of the method and shows that the algorithm performs much better than a simple genetic algorithms (SGA). 最后以一经典算例验证了该方法的良好特性。
AGA has carried out certainly adjustment and improvement on probability alternating and mutant probability in simple genetic algorithm mainly. AGA主要是对基本遗传算法中交叉概率和变异概率进行自适应调整和改进。
In the second section, the paper also proposes a new Hierarchic Genetic Algorithms (HGA), which overcome some drawbacks on simple Genetic Algorithms (SGA). 第二部分中，文章提出了改进SGA不足的HGA算法。
A eugenic evolution strategy was proposed to improve the searching efficiency of the conventional simple genetic algorithm (SGA). 针对常规遗传算法全局寻优效率偏低的弱点,提出了一种优进策略,用以改进常规遗传算法。
Simple Genetic Algorithm（SGA） has some disadvantages in the multi-peak functions optimization.So does the Quantum Genetic Algorithm（QGA）. 摘要传统遗传算法（SGA）在处理多峰值函数优化问题中存在局部收敛性的问题，最初的量子遗传算法（QGA）也存在这一问题。
In order to overcome these troubles, a composite particle swarm optimization (CPSO) using simple genetic algorithm (SGA) to optimize the control parameters was proposed. 今提出引入遗传算法，在粒子群算法的搜索过程中，逐代优选参数，包括惯性权值，加速常数，以此构建为复合粒子群优化算法。
Real examples show that AVSSGA is a common solution for nonlinear equation, compared with simple genetic algorithm (SGA).It can double computing efficiency. 实例计算与发动机仿真结果说明：所设计的变搜索域遗传算法可作为通用的非线性方程解法，相比较于简单遗传算法，能成倍提高计算效率；
Besides the ability of stochastic global searching of Simple Genetic Algorithm(SGA),the algorithm involves niche technology and the use of inverse operators. 该算法在基本遗传算法随机搜索能力基础上加入小生境技术,使用了倒位算子,实现简单,求解快速。
Testing two typical multimodal functions shows that the niche genetic algorithm has more powerful global searching ability and can converge more quickly than the simple genetic algorithm. 用该算法对两个典型多峰函数求解的测试结果表明，该算法较之基本遗传算法有更强的全局搜索能力和更快的收敛速度。
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。
The Simulated Annealing Algorithm act on the choice operator the crossover operator the mutation operator at the same time, so which can boost up self-adaptability of the simple Genetic Algorithm. 提出了基于网格的退火遗传算法(GSAG)，该算法的基本思想是利用网格在种群规模一定的情况下，能够保证初始群体的遍历性与多样性，它主要用于初始群体的产生，基于网格的退火遗传算法最大特点：模拟退火同时作用于选择算子、交叉算子以及变异算子从而在整体上增强对遗传算法的自适应性，更好地满足了优胜劣汰的自然进化法则。
Simple genetic algorithm has a slow convergence velocity in late evolution and gets premature convergence easily.To solve these problems, an immune learning based genetic algorithm (ILGA) is proposed. 摘要针对基本遗传算法在进化后期收敛速度慢、易早熟收敛的问题，提出一种基于免疫学习机制的遗传算法(ILGA)。
Aiming at the simple genetic algorithm’s bad astringency, a kind of hybrid genetic algorithm, which colligates the excellence of ameliorative genetic algorithm and bfgs is put forwarded. 5．提出了两种损伤诊断两步法：分层神经网络两步法和指标分析与反分析两步法。
Simple genetic algorithm(SGA) was designed by using C++ for aluminum keyhole PAW in vertical position,which is used to decide near-optimal settings of the welding process parameters. 本文采用C++语言设计适用于铝合金穿孔型等离子弧立焊的基本遗传算法，用来确定焊接工艺参数的近似最优化配置。
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。
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。
Section for the link to the Genetic Algorithms FAQ. 中遗传算法常见问题解答的链接部分。