the activation function. Out of the four input nodes, two were connected to the two light sensors, while the remaining were provided inputs from each of the ultrasonic and the colour sensors. The sensor readings were normalized to the range between 0 and 1 and then fed to the inputs of the controller ANN.

Scenarios
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The proposed algorithm was tested on a set of three scenarios explained below in the order of increasing complexity.\
*Scenario $S_1$ - Static Environment*: In this scenario, a light source (an incandescent bulb) was fixed at some minimum height in the centre of the arena. The objective was to make the robot move towards the light source (phototaxis) and then stay near it. If the robot encountered an obstacle, then it had to avoid it. Using a bare minimum of just a single US and one LS puts additional pressure on the algorithm while searching and evolving an optimum controller. For a single robot controller, the task of phototaxis together with obstacle avoidance thus becomes non-trivial. Similar to online evolutionary systems presented in [@heinerman2016line], the objective function used herein rewards behaviours ($Ab$) for each of the sub-goals achieved. The sub-goals herein include motion of 