Graph drawing by force-directed placement
Software—Practice & Experience
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Strongly typed genetic programming
Evolutionary Computation
Networks: An Introduction
Evolution of architectural floor plans
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
The evolution of higher-level biochemical reaction models
Genetic Programming and Evolvable Machines
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Automatic generation of graph models for complex networks by genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The pathways that relay sensory information within the brain form a network of connections, the precise organization of which is unknown. Communities of neurons can be discerned within this tangled structure, with inhomogeneously distributed connections existing between cortical areas. Classification and modelling of these networks has led to advancements in the identification of unhealthy or injured brains, however, the current models used are known to have major deficiencies. Specifically, the community structure of the cortex is not accounted for in existing algorithms, and it is unclear how to properly design a more representative graph model. It has recently been demonstrated that genetic programming may be useful for inferring accurate graph models, although no study to date has investigated the ability to replicate community structure. In this paper we propose the first GP system for the automatic inference of algorithms capable of generating, to a high accuracy, networks with community structure. We utilize a common cat cortex data set to highlight the efficacy of our approach. Our experiments clearly show that the inferred graph model generates a more representative network than those currently used in scientific literature.