  Crossover between neural networks is considered disruptive due to the strong
functional dependency between connection weights. We propose a modularity-based
linkage model at the weight level to preserve functionally dependent
communities (building blocks) in neural networks during mixing. A proximity
matrix is built by estimating the dependency between weights, then a community
detection algorithm maximizing modularity is run on the graph described by such
matrix. The resulting communities/groups of parameters are considered to be
mutually independent and used as crossover masks in an optimal mixing EA. A
variant is tested with an operator that neutralizes the permutation problem of
neural networks to a degree. Experiments were performed on 8 and 10-bit parity
problems as the intrinsic hierarchical nature of the dependencies in these
problems are challenging to learn. The results show that our algorithm finds
better, more functionally dependent linkage which leads to more successful
crossover and better performance.
