Evolving Hidden Genes in Genetic Algorithms for Systems Architecture Optimization
Evolving Hidden Genes in Genetic Algorithms for Systems Architecture Optimization
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DOI：
10.1115/1.4040207
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
O. Abdelkhalik;S. Darani
中科院分区：
文献类型：
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作者：
O. Abdelkhalik;S. Darani
隐藏基因的概念最近被引入到遗传算法（GAs）中，用于处理设计变量数量变化的系统架构优化问题。选择染色体中隐藏的基因决定了溶液的结构。本文介绍了两类基因选择（分配）隐藏基因的机制。这些机制决定了染色体在隐藏基因存在的情况下如何进化。在提出的机制中，为每个基因分配一个标签；这个标签决定了基因是否被隐藏。在第一类机制中，标签使用随机操作进化。通过数值试验，提出并比较了这一类别的八种不同变化。第二类介绍标签演化的逻辑操作。这两个类别都在木星空间任务的行星际轨迹优化问题以及数学优化问题上进行了测试。为了优化隐基因算法参数的选择，设计并进行了多个数值实验。数值结果表明，所提出的标签概念和分配机制使隐藏基因遗传算法（HGGA）能够找到更好的解。
The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions.