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With neural architecture search methods gaining ground on manually designeddeep neural networks -even more rapidly as model sophistication escalates-, theresearch trend shifts towards arranging different and often increasinglycomplex neural architecture search spaces. In this conjuncture, delineatingalgorithms which can efficiently explore these search spaces can result in asignificant improvement over currently used methods, which, in general,randomly select the structural variation operator, hoping for a performancegain. In this paper, we investigate the effect of different variation operatorsin a complex domain, that of multi-network heterogeneous neural models. Thesemodels have an extensive and complex search space of structures as they requiremultiple sub-networks within the general model in order to answer to differentoutput types. From that investigation, we extract a set of general guidelines,whose application is not limited to that particular type of model, and areuseful to determine the direction in which an architecture optimization methodcould find the largest improvement. To deduce the set of guidelines, wecharacterize both the variation operators, according to their effect on thecomplexity and performance of the model; and the models, relying on diversemetrics which estimate the quality of the different parts composing it.

Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components