The surprising little effectiveness of cooperative algorithms in parallel problem solving

Biological and cultural inspired optimization algorithms are nowadays part of the basic toolkit of a great many research domains. By mimicking processes in nature and animal societies, these general-purpose search algorithms promise to deliver optimal or near-optimal solutions using hardly any information on the optimization problems they are set to tackle. Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes. The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the worth of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping randomly chosen bits. We find that even for smooth landscapes that exhibit a single maximum, the evolutionary algorithms do not perform much better than the blind search due to the stochastic effects of the genetic roulette. The imitative learning is immune to this effect thanks to the deterministic choice of the fittest string in the population, which is used as a model for imitation. The tradeoff is that it is more prone to be trapped in local maxima than the evolutionary algorithms in the case of mildly rugged landscapes. In fact, in the case of rugged landscapes with a not too low density of local maxima, the blind search beats the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.