An evolutionary and graph-rewriting based approach to graph generation

This paper describes an evolutionary computation based graph rewriting approach to generating classes of graphs that exhibit a set of desired global features. A set of rules are used to generate, in a constructive manner, classes of graphs. Each rule represents a transformation from one graph to another. Each of these transformations causes local changes in the graph. Probabilities can be assigned to the rules which govern the frequency with which they will be applied. By assigning these probabilities correctly, one can generate graphs exhibiting desirable global features. However, choosing the correct probability distribution to generate the desired graphs is not an easy task for certain graphs and the task of finding the correct settings for these graphs may represent a difficult search space for the evolutionary algorithms. In order to generate graphs exhibiting desirable features, an evolutionary algorithm is used to find the suitable probabilities to assign to the rules. The fitness function rewards graphs that exhibit the desired properties. We show, using a small rule base, how a range of graphs can be generated.