Active exploration is a necessary component of a putative spatial representation system in the mammalian brain. We address the problem of how spatial exploratory behaviour is generated in rodents by combining an artificial neural network model of place coding with a multiobjective evolutionary algorithm that tunes the model parameters so as to maximise the efficiency of environment exploration. A central property of the spatial representation model is an online calibration between external visual cues and path integration, a widely accepted concept in theoretical accounts of spatial learning in animals. We find that the artificially evolved exploration model leads to recurrent patterns of exploratory behaviour in a way observed in experimental studies of spatial exploration in rodents. Our results provide a link between the functional organisation of the biological spatial learning network and the observed high-level patterns of exploratory behaviour.