Cellular Genetic Algorithms for Identifying Variables in Hybrid Gene Regulatory Networks

The hybrid modelling framework of gene regulatory networks (hGRNs) is a functional framework for studying biological systems, taking into account both the structural relationship between genes and the continuous time evolution of gene concentrations. The goal is to identify the variables of such a model, controlling the aggregated experimental observations. A recent study considered this task as a free optimisation problem and concluded that metaheuristics are well suited. The main drawback of this previous approach is that panmictic heuristics converge towards one basin of attraction in the search space, while biologists are interested in finding multiple satisfactory solutions. This paper investigates the problem of multimodality and assesses the effectiveness of cellular genetic algorithms (cGAs) in dealing with the increasing dimensionality and complexity of hGRN models. A comparison with the second variant of covariance matrix self-adaptation strategy with repelling sub-populations (RS-CMSA-ESII), the winner of the CEC'2020 competition for multimodal optimisation (MMO), is made. Results show evidence that cGAs better maintain a diverse set of solutions while giving better quality solutions, making them better suited for this MMO task.