当适应度函数和突变、重组及水平基因转移率已知时，该项新的数学上的结果可以使这一机制运算化。
The new mathematical results allow calculation of this mechanism when the fitness function and the mutation, recombination and horizontal gene transfer rates are known.
采用模糊集理论建立了适应度函数，并对装配序列进行评价和优化。
Then, fitness function was built according to fuzzy set theory and assembly sequence was evaluated and optimized.
通过给出适当的适应度函数，寻找出全局的最优解，并得到配准结果，这为医学临床诊断多模态信息融合提供了一种方法。
By giving suitable fitness function, global optimal solution was found and result of registration was given. A method was provided for clinical diagnosis.
该算法采用更适合无功优化特点的分组整数实数混合编码方式，并采用映射法计算适应度函数。
A grouped, integer and real number mixed coding method is applied in this algorithm. The mapped-method is adopted to calculate the fitness function.
同时，提出了取代时间系数的概念，可以定量地衡量不同的选择算子、适应度函数变换方式的作用。
The takeover time coefficient is given, so the influence of different selection operators and transformation of fitness function can be measured quantitatively.
对遗传算法作曲的适应度函数进行了研究。
The thesis makes research on the fitness functions of GA composing.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
常用的适应度函数的设计方法有线性变换法、幂函数变换法、指数变换法和方差调整法等。
In designing methods of fitness function there are many ways, for examples: linear transformation, power transformation, index transformation, variance adjustment, etc.
对设定的性能指标如超调量和调整时间的违背量将作为GASA适应度函数中的惩罚项。
The violations of specifications such as percent overshoot and settling time are penalized in the fitness function of GASA.
并对约束条件的处理和适应度函数的构造进行了研究，给出了算法的基本步骤和流程框图。
Then study the constraints handling, construction of fitness function and the algorithms basal steps and flow chart is given.
介绍了一种基于改进适应度函数的遗传单神经元控制方法。
A single neural node control based on genetic algorithm with improving fitness function is presented.
在基于GA的自动组题算法中，构建适应度函数是最大的一个难点。
Fitness function's building is the most difficult point in GA-based automatic test creation algorithms.
同时为了抛弃部分不可行点，设计了一个新的适应度函数，其仅仅依赖于个体的不可行度和目标函数值。
Meanwhile, in order to discard some infeasible individuals, a new fitness function is given based on objective function and the degree of constraint violations.
针对求解目标，我们设计了整合的适应度函数。
An integrated fitness function is designed to indicate optimized object.
将遗传算法应用于高功率微波模式的反演识别，并对适应度函数、控制参数作了分析。
The genetic algorithms are used for high power microwave model inversion recognition, at the same time, the paper analyze the sufficiency function and the controlled variable.
MFF中采用了有特色的概率相关系数对GEP中的适应度函数进行优化，使得精度提高了27%。
MFF optimizes the fitness function in GEP by special approach called probability correlation factor, which increases the precision by 27%.
通过构建适应度函数，根据信号适应度函数进行遗传选择，舍弃不稳定信号，对有用信号进行杂交，从而获得较高的定向精度。
The instable signal is abnegated and the steady signal is intercrossed for improving the precision through constructing adaptive function and the process of genetic selection.
就新的参数估计方法所提出多个适应度函数，进行了性能对比的模拟实验。
Based on the new parameter estimating method, we present several fit-functions, and make simulating experiments for the performance contrast.
通过适应度函数的评价，使进化后的设计方案满足客户的定制需求。
Through evaluation of fitness function, improved design scheme would satisfy customization needs.
通过适应度函数的评价，使进化后的设计方案满足客户的定制需求。
Through evaluation of fitness function, improved design scheme would satisfy customization needs.
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