is a technique which is a distributed, fully embodied and evolutionary version of such traditional approaches.

In this paper, we propose an immunology-inspired embodied action-evolution cum selection algorithm to evolve different subcontrollers for different regions of the search space. Sensor values sampled from the environment form the antigen while the associated subcontroller (actions) corresponds to an antibody. Similar to the way antibodies are evolved and primed for different antigens, the algorithm evolves and selects different subcontrollers for an associated region of antigenic (sensory) space. Some of the salient features of the proposed algorithm are:\
(1) On-the-fly and Onboard Evolution: The subcontrollers are evolved on-the-fly and onboard the robots.\
(2) Distributed Learning: While the encapsulated version of algorithm within one robot seeks and learns to choose the best subcontroller, sharing of these controllers across peers in the collective of robots speeds up the convergence process [@bredeche2017embodied].\
(3) Single Parameter Tuning: A single system parameter (explained later) can be used to tune and vary the granularity of search in the antigenic space.

This paper describes an alternative algorithm to evolve and select different subcontrollers on-the-fly for different ranges (portions) of sensory values (antigenic space) sampled from the environment, in a distributed and 