Genetic algorithm evaluation of green search allocation policies in multilevel complex urban scenarios

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 simple models. We then turn to more realistic urban models and show that the performance of GA increases with model complexity and uncertainty level.