Historically, most evolutionary algorithms (EAs) were designed to optimise a fitness function, solving a single problem without considerations for generalisation to unseen problems or robustness to perturbations to the evaluation environment. However, it was widely known that successfully converging to the maximum of that fitness function requires maintaining genetic diversity in the population of solutions (see e.g., Laumanns et al. (2002); Gupta and Ghafir (2012); Ursem (2002); Ginley et al. (2011)). Moreover, the use of niching demonstrated how maintaining subpopulations could help find multiple solutions to a single problem (Mahfoud, 1995). Some studies included genetic diversity as one of the objectives of the EA (Toffolo and Benini, 2003). Approaches in evolutionary robotics, artificial life, and neuro-evolution realised that genetic diversity does not necessarily imply a diversity of solutions, since (i) different genotypes may encode the same behaviour and vice versa (especially for complex genotypes such as neural networks); and (ii) many genotypes may encode unsafe or undesirable solutions that should be discarded during evolution (e.g., self-collisions on a multi-joint robot arm). Such approaches began to emphasise behavioural diversity (Mouret and Doncieux, 2009b; Gomez, 2009; Mouret and Doncieux, 2009a; Mouret, 2010), not only as a driver for objective-based evolution but also as the enabler for diversity-or novelty-driven evolution (Lehman and Stanley, 2011a). This work is the extended version of the paper: David M. Bossens & Danesh Tarapore (2021). On the use of feature-maps for improved quality-diversity meta-evolution.

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. To apply behaviour adaptation in swarm robotic systems, we propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. We set up foraging experiments with a variety of disturbances: injected faults to proximity sensors, ground sensors, and the actuators of individual robots, with 100 unique combinations for each type. We also investigate disturbances in the operating environment of the swarm, where the swarm has to adapt to drastic changes in the number of resources available in the environment, and to one of the robots behaving disruptively towards the rest of the swarm, with 30 unique conditions for each such perturbation. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within at most 30 evaluations.