Social media platforms face an ongoing challenge in combating the proliferation of social bots, automated accounts that are also known to distort public opinion and support the spread of disinformation. Over the years, social bots have evolved greatly, often becoming indistinguishable from real users, and more recently, families of bots have been identified that are powered by Large Language Models to produce content for posting. We suggest an idea to classify social users as bots or not using genetic similarity algorithms. These algorithms provide an adaptive method for analyzing user behavior, allowing for the continuous evolution of detection criteria in response to the ever-changing tactics of social bots. Our proposal involves an initial clustering of social users into distinct macro species based on the similarities of their timelines. Macro species are then classified as either bot or genuine based on genetic characteristics. The preliminary idea we present, once fully developed, will allow existing detection applications based on timeline equality alone to be extended to detect bots. By incorporating new metrics, our approach will systematically classify non-trivial accounts into appropriate categories, effectively peeling back layers to reveal non-obvious species.