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Through regulating the crossover and mutation rate, it overcomes the disadvantage of premature convergence in SGA. 通过对交叉、变异概率的动态调整，克服了SGA未成熟收敛的弊端。
Simulation results show that FCGA can overcome the premature convergence effectively and improve the speed of convergence. 对三个函数的优化结果表明:FCGA不仅能够有效克服早熟收敛,而且提高了算法的收敛速度。
The computation results show that both global and local searching abilities of the IGA are improved and premature convergence is overcome availably. 试验函数的计算表明，这一方法可以同时提高遗传算法的全局和局部搜索能力，并能有效地抑制早熟收敛的发生。
The knowledge about evolutionary process is not effectively abstracted and used in the genetic algorithm (GA) which is a premature convergence. 摘要传统遗传算法缺乏对进化过程知识的有效提取和利用，存在早熟收敛。
The hybrid genetic algorithm features less infeasible solution, higher convergence speed and can avoid premature convergence. 该算法能够有效地减少不可行解的产生，提高收敛速度，避免早熟收敛。
This algorithm has the advantages of fast convergence speed,small number of convergence and easy to get premature convergence. 该算法具有收敛速度快、迭代次数少且不易陷入不成熟收敛等优点。
A improved hybrid genetic algorithm (HGA) is proposed to overcome premature convergence and increase the optimize pace of SGA . 3.;为克服简单遗传算法容易早熟和收敛速度慢的缺陷;在简单遗传算法基础上提出一种改进的混合遗传算法。
To solve the premature convergence problem of the Particle Swarm Optimization (PSO), an improved PSO method was proposed. 针对粒子群优化算法早熟收敛现象，提出了一种改进的粒子群优化算法。
In order to solve the premature convergence problem of particle swarm optimization，a novel fuzzy adaptive Particle Swarm Optimization based on T-S model（T-SPSO） is presented. 摘要针对微粒群优化算法存在的早熟问题，提出了一种基于T-S模型的模糊自适应PSO算法（T-SPSO算法）。
Genetic algorithms is a kind of adaptive global optimization statistical algorithm, it is tend to emerge premature convergence, which affects the solving of problems. 摘要遗传算法是一种自适应全局优化概率算法，容易产生早熟（过早收敛）现象，影响了问题的求解。
Considering the premature convergence limitation of the basic particle swarm optimizer (PSO) algorithm, an improved adaptive PSO algorithm with mutation was proposed. 针对粒子群算法易早熟收敛的局限性，提出了一种带变异的改进自适应粒子群优化(PSO)算法。
The method is new way to solve premature convergence in MFO.ANN evolutionary modeling is MFO.The vality of ANN model is evaluated by its generalizationary ability. 该方法为遗传算法引入了基于模式的基因记忆、学习与收敛方向选择能力，并从理论上分析了其全局收敛性与计算效率，开拓了遗传算法化解早熟收敛现象的新研究方向。
The simulation results show that APGA can resolve premature convergence effectively and improve the search efficiency and result precision of genetic algorithm greatly. 仿真结果表明:这种算法能够有效地防止早熟收敛,可以进一步提高遗传算法的搜索效率和解的精度。
This paper demonstrates through analysis and experiment that the cataclysm operator can improve the diversity of the small size populations and avoid the premature convergence in Genetic Algorithm. 该文通过分析和实验表明,采用灾变算子可以提高遗传算法小规模群体的多样性,从而避免早熟收敛。
Diversity Enhancing Genetic Algorithm(DEGA)is presented to prevent premature convergence of GA,which introduced the ideas of affinity from immune system and cataclysm from biologic evolution process. 引入免疫系统“亲和度”的概念和生物进化过程中的“灾变”概念,提出一种强多样性的遗传算法来克服早熟收敛。
With the proposed penalty strategies, the genetic search process will maintain suitable balance between the population diversity and the selective pressure, and prevent premature convergence. 经由所提出之惩罚策略，使得搜寻过程中族群多样性与选择压力可维持适当的平衡，以避免早熟之收敛结果。
Proposes an improved inertia weight mutation particle swarm optimization to solve the premature convergence problem,and to avoid the slowconvergence in the later convergence phase. 针对微粒群优化算法的早熟收敛和进化后期收敛速度慢等问题,提出了一种改进惯性权重的变异微粒群优化算法。
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)。
Considering the easy premature convergence and schema deceptive problem in SGA(Standard Genetic Algorithms),the Selection-based Genetic Algorithm(GA_S) is proposed to improve the genetic operation. 鉴于标准遗传算法比较容易产生早熟现象和模式欺骗而收敛于局部最优解,论文对标准遗传算法的遗传操作进行了改进,提出了基于选择的遗传算法(GA_S)。