  Genetic algorithms have played an important role in engineering optimization.
Traditional GAs treat each gene separately. However, biophysical studies of
gene regulatory networks revealed direct associations between different genes.
It inspires us to propose an improvement to GA in this paper, Gene Regulatory
Genetic Algorithm (GRGA), which, to our best knowledge, is the first time to
utilize relationships among genes for improving GA's accuracy and efficiency.
We design a directed multipartite graph encapsulating the solution space,
called RGGR, where each node corresponds to a gene in the solution and the edge
represents the relationship between adjacent nodes. The edge's weight reflects
the relationship degree and is updated based on the idea that the edges'
weights in a complete chain as candidate solution with acceptable or
unacceptable performance should be strengthened or reduced, respectively. The
obtained RGGR is then employed to determine appropriate loci of crossover and
mutation operators, thereby directing the evolutionary process toward faster
and better convergence. We analyze and validate our proposed GRGA approach in a
single-objective multimodal optimization problem, and further test it on three
types of applications, including feature selection, text summarization, and
dimensionality reduction. Results illustrate that our GARA is effective and
promising.
