was empirically found and set to 0.45, 0.4 and 0.25 for the scenarios $S_1$, $S_2$ and $S_3$, respectively. For a given scenario, the desired value of $\epsilon$ can be found by first initializing it to half the normalized range. The value may then be gradually increased or decreased based on whether the number of subcontrollers generated is sufficient to make the robot(s) learn the scenario. Using the proposed algorithm, the subcontrollers were allowed to evolve for 200 iterations in scenario $S_1$ while the same was 500 for the other two scenarios.\
*1) Scenario $S_1$*: Fig. \[fig:s1\] shows a typical evolution-selection curve of the antibodies created, evolved and selected during one of the trials. The X-axis denotes the iteration count and Y-axis indicates the fitness values returned by the objective function defined in equation \[eq:t1\].

For this particular run, the robot was initially placed near the boundary wall of the arena, facing the light source. Following is a description of the events that resulted in the graph shown in Fig. \[fig:s1\]. The robot initially sampled the $Ag$ from its environment and the first antibody $Ab_1$ was created, depicted by the cyan coloured marker on the graph (Fig. \[fig:s1\]). The robot then 