  This paper presents a novel method, called Modular Grammatical Evolution
(MGE), towards validating the hypothesis that restricting the solution space of
NeuroEvolution to modular and simple neural networks enables the efficient
generation of smaller and more structured neural networks while providing
acceptable (and in some cases superior) accuracy on large data sets. MGE also
enhances the state-of-the-art Grammatical Evolution (GE) methods in two
directions. First, MGE's representation is modular in that each individual has
a set of genes, and each gene is mapped to a neuron by grammatical rules.
Second, the proposed representation mitigates two important drawbacks of GE,
namely the low scalability and weak locality of representation, towards
generating modular and multi-layer networks with a high number of neurons. We
define and evaluate five different forms of structures with and without
modularity using MGE and find single-layer modules with no coupling more
productive. Our experiments demonstrate that modularity helps in finding better
neural networks faster. We have validated the proposed method using ten
well-known classification benchmarks with different sizes, feature counts, and
output class count. Our experimental results indicate that MGE provides
superior accuracy with respect to existing NeuroEvolution methods and returns
classifiers that are significantly simpler than other machine learning
generated classifiers. Finally, we empirically demonstrate that MGE outperforms
other GE methods in terms of locality and scalability properties.
