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The Application of Dominant-Recessive Diploid Codes in MOGA

机译:显性隐性二倍体代码在MOGA中的应用

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When solving an optimal problem, different encoding method has an important effect on the performance of multi-objective genetic algorithm. This paper breaks the traditional binary coding ideas, introduces a new dominant-recessive diploid codes which applied in the MOGA. we analyses the impact on solution space by the binary multi-objective genetic algorithm and dominant-recessive diploid codes multi-objective genetic algorithm, which is operated by three basic operators of tournament selection, two-point crossing, and the basic bit mutation. Furthermore, by using the Numerical experiments of three Classic multi-objective optimization test functions, this algorithms and the efficient binary multi-objective algorithms named niched pare to genetic algorithms are compared. From the solution, we know that this paperȁ9;s algorithm is obviously superiors to the niched pare to genetic algorithm about the distribution, convergence of solution and the capability of anti-prematurity. Thus, it is interpreted that the algorithm is feasible from the aspects of theoretic analysis and numerical experiments, and the pare to solutions can be came to.
机译:解决最优问题时,不同的编码方法对多目标遗传算法的性能有重要影响。本文突破了传统的二进制编码思想,介绍了一种新的显性-隐性二倍体编码,该编码在MOGA中得到了应用。我们分析了由二进制多目标遗传算法和显性隐性二倍体代码多目标遗传算法对解空间的影响,该算法由锦标赛选择,两点交叉和基本位变异的三个基本算子操作。此外,通过使用三个经典的多目标优化测试函数的数值实验,将该算法与有效的二进制多目标算法niched pare与遗传算法进行了比较。从解中可以看出,在解的分布,收敛性和抗早熟能力方面,本文的9算法明显优于传统的遗传算法。因此,从理论分析和数值实验的角度解释了该算法是可行的,并且可以求得解决方案。

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