This paper investigates the relationship between the underlying complexity of urban agent-
based models and the performance of optimisation algorithms. In particular, we address the
problem of optimal green space allocation within a densely populated urban area. We find
that a simple monocentric urban growth model may not contain enough complexity to be
able to take complete advantage of advanced optimisation techniques such as genetic
algorithms (GA) and that, in fact, simple greedy baselines can find a better policy for these …