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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. 

The concept of hidden genes was recently introduced in genetic algorithms to handle variable-size design space optimization problems. This paper presents new developments in hidden genes genetic algorithms. Mechanisms for assigning (selecting) the hidden genes in the chromosomes of genetic algorithms are presented. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not, while they evolve over generations using stochastic operations. These mechanisms are tested on mathematical optimization problems and on a trajectory optimization problem for a space mission to Jupiter. In the conducted tests, one of the proposed hidden genes assignment mechanism has enabled the hidden genes genetic algorithms to find better (lower cost) solutions, while other mechanisms has shown to be able to find close solutions.