Visual homing is a short-range robot navigation method which can lead an agent to a position with accuracy, provided that the majority of the scene visible from the home position is also visible from the current robot position. Recently Zeil, Hoffmann and Chahl (2003) showed that a simple calculation— the root mean square (RMS) difference between the current image and the home image—produces a monotonic function leading to the home position for natural images. In this article we propose a gradi ent descent algorithm based on Caenorhabditis elegans chemotaxis (Ferree & Lockery, 1999) for hom ing with the RMS signal. The parameters for this algorithm are evolved for a simulated agent, and the resulting homing behavior compared with alternative algorithms in simulation and using a real robot. A simulated agent using this algorithm in an environment constructed from real world images homes effi ciently and shows generalization to variations in lighting and changes in the scene. In the real robot this algorithm is affected by noise resulting from imperfect sensors, and alternative algorithms appear more robust. However, the best performing algorithm for unchanging environments, image warping (Franz, Schölkopf, Mallot, & Bülthoff, 1998), is completely disabled by scene changes that do not affect algo rithms utilizing the RMS difference.