One of the main problems of symbolic regression methods is how to encode mathematical expressions to make the structure concise and readable. Here we address to the problem of network operator's matrix scarcity, which we resolve using python's standard types. Our approach allows to get rid of zero elements bellow the main diagonal. Moreover, we introduce new parametric variation, which helps us to tune parameters during structural evolving. We provide a description of the network operator and algorithms for finding the optimal solution using the principle of small variations of the basis solution. Our new encoding scheme and the complex functional for the group control task then described. In the experimental part we introduce a new way to apply network operator with a variable number of arguments to the group control system synthesis problem. Along with this we use for the first time individual network operators for each robot. From the experiment we can see that provided tricks lead to results that are not inferior in the case of usual network operator shared by all robots. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 13th International Symposium “Intelligent Systems” (INTELS'18).