Searching with cooperators

Biological evolution serves as a blueprint for the design of search and optimization algorithms, and has generated vast number of research prototypes as well as industrial implementations since it was first proposed to solve complex engineering problems the 1960s [1–3]. Underlying this is the idea that the evolutionary forces of mutation, recombination, reproduction and selection can drive the population towards better adapted solution over time, effectively solving an optimization problem by navigating the fitness landscape they inhabit. A much overlooked evolutionary force in the design of better algorithms so far has been cooperation, shown to be crucial to shape individual and group behavior at multiple scales of interaction. Here we explore the ability of cooperative dynamics to further increase the efficiency of evolutionary strategies. For this, we perform computer experiments on a variety of landscapes of increasing complexity. We present evidence suggesting that cooperative dynamics are able to naturally balance exploration and exploitation of local maxima, via endless cycles of cooperation (where local maxima are exploited) and defection (where new areas are explored). Cooperative strategies prove to be more robust to landscape ruggedness than evolutionary strategies which never cooperate, always cooperate, or cooperate randomly. Furthermore, our simulations show that the cooperative dynamics are invariant to the complexity of the landscape, hinting at the possibility that cooperation strategies may be able to absorb and exploit local information to keep the exploration–exploration tradeoff invariant across a range of environments.