based on $\epsilon$ which in turn defines the area of the *Active Region* ($AR$) of a subcontroller (antibody). More the value of $\epsilon$, more is the environment space catered to by that subcontroller. Increasing $\epsilon$ will mean lesser number of subcontrollers for a given environment space. If $\epsilon$ were to cover the entire environment space then, the algorithm would try to evolve just one subcontroller that can cater to all conditions, as in [@heinerman2016line; @o2014distributed]. On the other hand, a very low value of $\epsilon$ would mean a smaller $AR$ resulting in too many subcontrollers catering to very specific tasks.\
**(1+1)-Online Evolutionary Algorithm:** The evolution of the $Ctr$ of the best $Ab$ that has been selected can be achieved using an Evolutionary Algorithm (EA). We have leveraged the (1+1)-Online evolutionary strategy [@bredeche2009line] to evolve the antibodies with controllers that deliver satisfactory performance. In this strategy, the controller weights are mutated using a Gaussian function with $N(0,\sigma)$, where the value of $\sigma$ doubles if the offspring of the controller of the selected $Ab$ performs lower than the currently used parent controller. An offspring replaces the parent if the former outperforms the latter one.

Experiments
===========

For experimentation, we have used a 