$Pt$ and $Ep$ are dimensionally equivalent and have the same shape space. The $Ctr$ is a vector comprising the weights of an associated neural network while the $C$ denotes the fitness value returned after executing the $Ctr$ when the corresponding $Ab$ is chosen to quell $Ag$. The fitness function depends on the task and is provided by the experimenter based on the application. $Ab$s, with better performing $Ctr$s, evolved based on fitness values, are assigned proportionately higher concentrations. The ID number aids in uniquely identifying an $Ab$. Fig. \[fig:ag\_ab\] also shows another $Ag$ (bottom) which falls in the $AR$ of the same $Ab$.

![An Antibody tackling two different Antigens ($SN_i$s are the values obtained from sensors onboard the robot)[]{data-label="fig:ag_ab"}](ab_ag.png)

The Euclidean distance between an $Ep$ and a $Pt$ is used as the affinity measure ($\psi$). Lesser this distance more is the affinity. An $Ab$ is chosen to tackle a given environment state ($Ag$) if the affinity $\psi$ between the $Ep$ and the $Pt$ is less than $\epsilon$, a system constant akin to the cross-reactivity threshold [@de2002artificial]. Antibodies that satisfy this criterion are referred to as the *candidate antibodies*. All such candidates antibodies are stimulated by the corresponding antigen $Ag$ located 