to be learned are complex for a single controller to cater to. In such scenarios where a complex task may contain bootstrap [@gomez1997incremental] and deception [@whitley1991fundamental] issues, researchers have opted to use *behaviour-decomposition* [@silva2016open] based techniques. In behaviour-decomposition methods, separate subcontrollers are evolved which can cater to different subtasks. An arbitrator that sits on top of these subcontrollers and performs the task of selecting the appropriate subcontroller for the current task [@duarte2012hierarchical; @lee1999evolving], is evolved.

In contrast to traditional ER approaches, the term Embodied Evolution (EE) applies to a collective of robots that autonomously and continuously adapt their behaviour in accordance with the changes in the environment [@bredeche2017embodied]. In EE, robot controllers learn both onboard and online, and continue to do so even when the robots are deployed in an environment, thereby reducing the *reality gap* [@jakobi1997evolutionary]. EE is still susceptible to bootstrapping and deception issues when the task to be accomplished is complex for single-controller based robots to learn. While under traditional approaches, there are evidence of earlier ER work that use behavioural decomposition [@lee1999evolving] to avoid the bootstrap issues but these techniques have been seldom applied in an *embodied* (online and onboard) manner. What needs to be explored 