L-systems and artificial chemistry to develop digital organisms

With the purpose of populating virtual worlds with various adapted artificial organisms, we propose an ontogenetic and phylogenetic hybrid model to generate complete organisms possessing metabolism, morphology, and behavior from a single initial cell. The initial purpose of our work is to generate organisms that are thereafter used to define complete organisms. In this paper, we introduce a bio-inspired cellular developmental model that links different approaches of ontogenesis systems: grammatical and cell chemistry approaches. Thus, we propose an alternative to parametric L-systems (APL-systems) in order to simulate morphogenesis of organisms according to their internal states. The developed organisms have a metabolism using environmental substrates to grow and to act. Moreover, they are able to exhibit almost perfect self-healing characteristics afterwards or even during their development.

[1]  Yves Duthen,et al.  A cell pattern generation model based on an extended artificial regulatory network , 2008, Biosyst..

[2]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[3]  Stefano Nolfi,et al.  18 – Artificial life models of neural development , 2003 .

[4]  Hervé Luga,et al.  From Single Cell to Simple Creature Morphology and Metabolism , 2008, ALIFE.

[5]  R. Beer,et al.  20 – A developmental model for the evolution of complete autonomous agents , 2003 .

[6]  Maja J. Matarić,et al.  A Developmental Model for the Evolution of Complete Autonomous Agents , 1996 .

[7]  Karl Sims,et al.  Evolving 3d morphology and behavior by competition , 1994 .

[8]  Hervé Luga,et al.  Cell2Organ: Self-repairing artificial creatures thanks to a healthy metabolism , 2009, 2009 IEEE Congress on Evolutionary Computation.

[9]  A. M. Turing,et al.  The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[10]  S. Smith,et al.  A Learning System Based on Genetic Algorithms , 1980 .

[11]  Maciej Komosinski.  Framsticks: A Platform for Modeling, Simulating, and Evolving 3D Creatures , 2005 .

[12]  R. Pfeifer,et al.  Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny , 2001 .

[13]  Hervé Luga,et al.  A New Step for Artificial Creatures , 2007, 2007 IEEE Symposium on Artificial Life.

[14]  Jordan B. Pollack,et al.  TITLE : Generative Representations for the Automated Design of Modular Physical Robots , 2003 .

[15]  Wolfgang Banzhaf,et al.  Artificial ChemistriesA Review , 2001, Artificial Life.

[16]  Hugo de GARIS.  ARTIFICIAL EMBRYOLOGY AND CELLULAR DIFFERENTIATION , 1999 .

[17]  Larry D. Pyeatt,et al.  A comparison between cellular encoding and direct encoding for genetic neural networks , 1996 .

[18]  A. Lindenmayer.  Mathematical models for cellular interactions in development. I. Filaments with one-sided inputs. , 1968, Journal of theoretical biology.

[19]  Gabriela Ochoa,et al.  Evolving L-Systems to Capture Protein Structure Native Conformations , 2005, EuroGP.

[20]  Stefan Bornhofen,et al.  Competition and evolution in virtual plant communities: a new modeling approach , 2009, Natural Computing.

[21]  W. Banzhaf.  Artificial Regulatory Networks and Genetic Programming , 2003 .

[22]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[23]  A. Barr,et al.  A Simulation Testbed for the Study of Multicellular Development: The Multiple Mechanisms of Morphogenesis , 2008 .

[24]  Risto Miikkulainen,et al.  A Taxonomy for Artificial Embryogeny , 2003, Artificial Life.

[25]  Martin V. Butz,et al.  Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule , 2005, IWLCS.

[26]  Borys Wróbel,et al.  Evolution of the Morphology and Patterning of Artificial Embryos: Scaling the Tricolour Problem to the Third Dimension , 2009, ECAL.

[27]  Hod Lipson,et al.  Molecubes Extended : Diversifying Capabilities of Open-Source Modular Robotics , 2008 .

[28]  Julian Francis Miller,et al.  Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision , 2010, GECCO '10.

[29]  Alessandro Fontana,et al.  Epigenetic Tracking: Biological Implications , 2009, ECAL.

[30]  Phil Husbands,et al.  Asymmetric cell division and its integration with other developmental processes for artificial evolutionary systems , 2004 .

[31]  Gabriella Kókai,et al.  Modelling Blood Vessels of the Eye with Parametric L-Systems Using Evolutionary Algorithms , 1999, AIMDM.

[32]  Peter J. Bentley,et al.  Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem , 1999, GECCO.

[33]  P. Prusinkiewicz,et al.  Constraints of space in plant development. , 2009, Journal of experimental botany.

[34]  René Doursat,et al.  Facilitating evolutionary innovation by developmental modularity and variability , 2009, GECCO.