本文探讨了多目标遗传算法存在的问题，并提出了相应的改进策略。
This paper discusses some problems of Multi-objective Genetic Algorithms at the same time, gives some new improvements to MOGAs.
以多目标遗传算法为代表的进化算法被认为特别适合求解此类问题。
The Multi-objectives Genetic Algorithms (MOGA) as representative evolution algorithms was considered specially suit to solve this kind of questions.
一个多目标遗传算法的优劣主要看三个指标：解集收敛程度，解集分布度以及时间消耗。
The performance of an MOGA can be measured from three aspects: the convergence to the true Pareto optimal front, the diversity of solutions and the time consuming.
为了获得更精简的特征序列，去除冗余特征，在特征约减方面，提出了基于多目标遗传算法的虹膜融合特征约减方法；
To get more precise fusion features by clearing redundant features, we propose a feature selection method based on multi-object evolution algorithm.
作者提出了一种基于遗传算法的火电单元机组多目标优化协调控制策略。
The authors propose a coordinative control strategy of multiobjective optimal in power unit based on genetic algorithm(GA).
由于参数对控制变量的影响比较复杂，为了得到较好的控制效果采用遗传算法在多目标函数约束下进行优化。
In order to achieve good control effect, Generic Algorithm is adopted to solve such a complex problem with some many parameters.
遗传算法在解决多目标优化问题中有其独特的优势，而航天器轨道机动是典型的多目标优化问题。
Genetic algorithms to solve the multi-objective optimization problems have their unique advantages, and the spacecraft orbit maneuver was a typical multi-objective optimization problem.
就一个简单算例，着重探讨如何将遗传算法应用于解决多目标模糊问题。
Takes a simple question for example to discuss how to apply the genetic algorithms to solve the multi-objectives and fuzzy problems.
就一个简单算例，着重探讨如何将遗传算法应用于解决多目标模糊问题。
Takes a simple question for example to discuss how to apply the genetic algorithms to solve the multi-objectives and fuzzy problems.
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