within the overlap of all active regions of these antibodies as shown in Fig. \[fig:shapeSpace\](c). This antigenic stimulation ($AgSti$) results in increasing the $C$ of all the candidate antibodies by an amount proportional to their respective $\psi$ values. In the computational world, a specific subcontroller is synonymous to an $Ab$ while an environmental state acts as an $Ag$. Thus, different subcontrollers could be evolved, each of which is tuned to specific environmental states. In this paper, we present a BIS inspired algorithm to evolve subcontrollers (antibodies) for specific actions required to counter different environment states (antigens) sampled by the sensors onboard a robot. Just as the BIS, this algorithm is distributed in nature and evolves subcontrollers on-the-fly.

The Proposed Algorithm
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The proposed algorithm is outlined in Algorithm \[algo:1\]. The algorithm runs on every robot belonging to the swarm. It works in tandem with a communication routine which facilitates foreign antibodies (subcontrollers) to be received from the peer robots in the collective and stored in an *extrinsic* antibody repertoire ($XRep$) within the robot. During the task execution by a robot, its *intrinsic* antibody repertoire ($IRep$) is broadcast to all the peer robots within a communication range. Instead of all, only 