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Evolving Neural Networks (NNs) has recently seen an increasing interest as analternative path that might be more successful. It has many advantages comparedto other approaches, such as learning the architecture of the NNs. However, theextremely large search space and the existence of many complex interactingparts still represent a major obstacle. Many criteria were recentlyinvestigated to help guide the algorithm and to cut down the large searchspace. Recently there has been growing research bringing insights from networkscience to improve the design of NNs. In this paper, we investigate evolvingNNs architectures that have one of the most fundamental characteristics ofreal-world networks, namely the optimal balance between connections cost andinformation flow. The performance of different metrics that represent thisbalance is evaluated and the improvement in the accuracy of putting moreselection pressure toward this balance is demonstrated on three datasets.

Evolving Neural Networks with Optimal Balance between Information Flow and Connections Cost