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The structure of the network underlying many complex systems, whetherartificial or natural, plays a significant role in how these systems operate.As a result, much emphasis has been placed on accurately describing networksusing network theoretic metrics. When it comes to generating networks withsimilar properties, however, the set of available techniques and propertiesthat can be controlled for remains limited. Further, whilst it is becomingclear that some of the metrics currently used to control the generation of suchnetworks are not very prescriptive so that networks could potentially exhibitvery different higher-order structure within those constraints, networkgenerating algorithms typically produce fairly contrived networks and lackmechanisms by which to systematically explore the space of network solutions.In this paper, we explore the potential of a multi-objective novelty-biased GAto provide a viable alternative to these algorithms. We believe our resultsprovide the first proof of principle that (i) it is possible to use GAs togenerate graphs satisfying set levels of key classical graph theoreticproperties and (ii) it is possible to generate diverse solutions within theseconstraints. The paper is only a preliminary step, however, and we identify keyavenues for further development.

Using novelty-biased GA to sample diversity in graphs satisfying constraints