Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks

Abstract

Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this drawback, object-oriented GP (OOGP) introduced a means of evolving programs with multiple behaviours which could be easily extended to state-based programs. However, the production of programs which allowed embedded knowledge and produced readable code was still not easily addressed using the OOGP methodology. Exemplified through the evolution of graph models for complex networks, this paper demonstrates the benefits of a new approach to OOGP inspired by abstract classes and linear GP. Furthermore, the new approach to OOGP, named LinkableGP, facilitates the embedding of expert knowledge while also maintaining the benefits of OOGP.