Microswimmers learning chemotaxis with genetic algorithms
Benedikt Hartl, Maximilian Hübl, Gerhard Kahl, Andreas Zöttl
Abstract
Significance Swimming microorganisms and migrating cells have developed various strategies in order to move in nutrient-rich or other chemical environments. We apply a genetic algorithm to the internal decision-making machinery of a model microswimmer and show how it learns to approach nutrients in static and dynamic environments. Strikingly, the emerging dynamics resembles the well-known run-and-tumble motion of swimming cells. We demonstrate that complex locomotion and navigation strategies in chemical environments can be achieved by developing a surprisingly simple internal machinery, which in our case, is represented by a small artificial neural network. Our findings shed light on how small organisms have developed the capability to conduct environment-dependent tasks.