The study of gene regulatory networks (GRNs) allows us to better understand biological systems such as the adaptation of the organism to a disturbance in the environment. Hybrid GRNs (hGRNs) are of interest because they integrate the continuous time evolution in GRN modeling which is convenient in biology. This study focuses on the problem of identifying the variables of hGRN models. In a large-scale case, previous work using constraint-based programming has failed to solve the minimal constraints on such variables which reflect the biological knowledge on the system behavior. In this work, we propose to transform a Constraint Satisfaction Problem (CSP) into a Free Optimization Problem (FOP) by formulating an adequate fitness function and validate the approach on an abstract model of the circadian cycle. We compare several continuous optimization algorithms and show that these first experimental results are in agreement with the specifications coming from biological expertise: evolutionary algorithms are able to identify a solution equivalent to the ones found by continuous constraint solvers.