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Crossover between neural networks is considered disruptive due to the strongfunctional dependency between connection weights. We propose a modularity-basedlinkage model at the weight level to preserve functionally dependentcommunities (building blocks) in neural networks during mixing. A proximitymatrix is built by estimating the dependency between weights, then a communitydetection algorithm maximizing modularity is run on the graph described by suchmatrix. The resulting communities/groups of parameters are considered to bemutually independent and used as crossover masks in an optimal mixing EA. Avariant is tested with an operator that neutralizes the permutation problem ofneural networks to a degree. Experiments were performed on 8 and 10-bit parityproblems as the intrinsic hierarchical nature of the dependencies in theseproblems are challenging to learn. The results show that our algorithm findsbetter, more functionally dependent linkage which leads to more successfulcrossover and better performance.

Modularity based linkage model for neuroevolution