Using DNA Analogy in Genetic Algorithms Instead of RNA Analogy

Drawing inspiration from natural selection and genetic principles, genetic algorithms serve as heuristic global optimization tools.They excel in tackling intricate optimization and simulation challenges that conventional methods struggle to solve.While genetic algorithms traditionally align more with RNA concepts, this paper introduces a DNA-inspired adaptation of these algorithms.The study validates this modification through programmatic implementation, leveraging the openGA opensource library, and assesses its efficacy using established optimization benchmark functions.The C/C++ source code is executed on a mobile device running the postmarketOS Linux distribution.