work by Heinerman et al. [@heinerman2016line] implements a relatively complex task of foraging using a single controller. A robot may require learning several such tasks. Under such conditions, the subcontroller will need to be re-trained to take in the set of new tasks. In an ANN-based subcontroller, such re-training may not be a viable exercise. Intuitively, one may conclude that evolving a single subcontroller to cater to an ever-increasing number of tasks, is extremely cumbersome if not impossible. Using behaviour primitives and an associated arbitrator may be a logical step to circumvent this drawback. However, the drawback is that for every incremental addition of a new subtask the behaviour arbitrator has to be trained offline all over again. This calls for an online and onboard continuous learning mechanism for both the subcontrollers and that arbitrator which will consequently lower the reality gap. The algorithm proposed in this paper distinguishes itself from earlier presented techniques in a way that instead of subtask division, it keeps dividing the whole sensor-sampled search area within the given environment, into separate regions. A subcontroller is then evolved on-the-fly for each such region.

Methodology
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The method proposed in this paper is inspired by the novel 