A Comparison of Genetic Regulatory Network Dynamics and Encoding

Jean Disset (1) , Dennis Wilson , Sylvain Cussat-Blanc (1, 2) , Stephane Sanchez (1, 2) , Hervé Luga (1, 3) , Yves Duthen (1, 2)

1 IRIT-REVA - Real Expression Artificial Life
2 UT Capitole - Université Toulouse Capitole
3 UT2J - Université Toulouse - Jean Jaurès

Genetic Regulatory Networks (GRNs) implementations have a high degree of variability in their details. Parameters, encoding methods, and dynamics formulas all differ in the literature, and some GRN implementations have a high degree of model complexity. In this paper, we present a comparative study of different implementations of a GRN and introduce new variants for comparison. We use a modified Genetic Algorithm (GA) to evaluate GRN performance on a number of common benchmark tasks, with a focus on real-time control problems. We propose an encoding scheme and set of dynamics equations that simplifies implementation and evaluate the evolutionary fitness of this proposed method. Lastly, we use the comparative modifications study to demonstrate overall enhancements for GRN models.

Mots clés: Fitness evaluations; Representations; Genetic algorithms

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Jean Disset, Dennis Wilson, Sylvain Cussat-Blanc, Stephane Sanchez, Hervé Luga, et al.. A Comparison of Genetic Regulatory Network Dynamics and Encoding. Genetic and Evolutionary Computation COnference (GECCO 2017), Jul 2017, Berlin, Germany. pp.91-98, ⟨10.1145/3071178.3071322⟩. ⟨hal-01912802⟩