urbanists but never quantitatively demonstrated.

In conclusion, this case study is encouraging as it proposes a concrete methodology of optimal planning with respect to criteria that are relevant to a particular situation. It could be used by generically planners in similar situations, while remaining cautious on the conditions of its applicability. We discuss this point next.

![Scatterplot of all configurations in the $(H,A)$ morphological plane. A Pareto front (red circles) is apparent in the bottom left part of the plot: it corresponds to “optimal” configurations trying to minimize both $H$ and $A$ objectives. The real situation (blue circle in $H=0.067, A=0.76$) corresponds to Fig. \[fig\_atlantis\] and is not far from this front. Points are colored according to their level of heterogeneity $\lambda$, from low (black) to high (yellow). More homogeneous configurations are concentrated in the central cluster, whereas Pareto points and their neighbors have higher heterogeneity levels. This lends support to the principle of “functional diversity”, which is often adopted by planners and urbanists today but has never been backed up by computational simulations.[]{data-label="fig_pareto"}](ParetoLastGoodRealPoint){width="\columnwidth"}

Discussion {#sec_discussion}
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The reproduction of typical urban morphologies and the possible application to a real-world problem shown in the previous sections indicate that a model 