1. A Comparison of Genetic Regulatory Network Dynamics and Encoding
- Author
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Jean Disset, Sylvain Cussat-Blanc, Yves Duthen, Hervé Luga, Dennis G. Wilson, Stéphane Sanchez, Real Expression Artificial Life (IRIT-REVA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse - Jean Jaurès (UT2J), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Institut National Polytechnique de Toulouse - INPT (FRANCE)
- Subjects
0301 basic medicine ,Scheme (programming language) ,Theoretical computer science ,Computer science ,02 engineering and technology ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Set (abstract data type) ,03 medical and health sciences ,Traitement des images ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Encoding (memory) ,Genetic algorithm ,0202 electrical engineering, electronic engineering, information engineering ,Traitement du signal et de l'image ,H- INFORMATIQUE ,Synthèse d'image et réalité virtuelle ,computer.programming_language ,Degree (graph theory) ,business.industry ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Vision par ordinateur et reconnaissance de formes ,Intelligence artificielle ,Genetic algorithms ,Network dynamics ,[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] ,Representations ,030104 developmental biology ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Fitness evaluations ,computer - Abstract
International audience; 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.
- Published
- 2017