Parameter acquirement methods for rule-based model of virtual plant based on optimal algorithms

Rule-based model is an effective technique to dynamically simulate the morphological development of a plant. It is thus used widely in the field of plant modeling and visualization. Before a virtual plant model with high quality performance is established, it is a key step to provide suitable parameters for the rule-based model. There are several disadvantages in the traditional/manual ways to design the model, e.g. with low efficiency. Therefore, how to obtain appropriate parameters for the rule-based model has attracted many researchers devoting themselves to this area. In the past twenty years, Genetic Algorithm and Gene Expression Programming have been used to optimize the production rules of Do L-system and Parametric Do L-system. Due to the complexity of the structure of a plant, researches' attentions are mostly paid in the narrow area of simple plant morphology restrictively. In this study, the parameter-acquired methods for rule-based model, which is based on Genetic Algorithm and Gene Expression Programming, are summarized. And the relative techniques and the possible development in the future are discussed as well.

[1]  Yan Guo,et al.  New advances in virtual plant research , 2001 .

[2]  Stefan Bornhofen,et al.  Evolving CSR Strategies in Virtual Plant Communities , 2008, ALIFE.

[3]  Una-May O'Reilly,et al.  Integrating generative growth and evolutionary computation for form exploration , 2007, Genetic Programming and Evolvable Machines.

[4]  Christian Jacob,et al.  Genetic L-System Programming , 1994, PPSN.

[5]  Jim Hanan,et al.  Statistical data directed evolution of L-system models for botanical trees , 2004 .

[6]  P. Prusinkiewicz.  Modeling plant growth and development. , 2004, Current opinion in plant biology.

[7]  Ricki Blau,et al.  Approximate and probabilistic algorithms for shading and rendering structured particle systems , 1985, SIGGRAPH.

[8]  Jon McCormack,et al.  Aesthetic Evolution of L-Systems Revisited , 2004, EvoWorkshops.

[9]  Yi-Ping Phoebe Chen,et al.  Derivation of L-system Models from Measurements of Biological Branching Structures Using Genetic Algorithms , 2002, IEA/AIE.

[10]  Alexandre Hardy,et al.  Generating plants with gene expression programming , 2007, AFRIGRAPH '07.

[11]  Marc Jaeger,et al.  Plant models faithful to botanical structure and development , 1988, SIGGRAPH.

[12]  Christian Jacob,et al.  Genetic L-System Programming: Breeding and Evolving Artificial Flowers with Mathematica , 1995 .

[13]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

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

[15]  Xuejin Chen,et al.  Sketch-based tree modeling using Markov random field , 2008, SIGGRAPH 2008.

[16]  Christophe Godin,et al.  A distance measure between plant architectures , 2000 .

[17]  Jon McCormack,et al.  Interactive evolution of L-System grammars for computer graphics modelling , 1993 .

[18]  Gabriela Ochoa,et al.  On Genetic Algorithms and Lindenmayer Systems , 1998, PPSN.

[19]  Jianxiong Xiao,et al.  Single image tree modeling , 2008, SIGGRAPH 2008.

[20]  Peter Oppenheimer,et al.  Real time design and animation of fractal plants and trees , 1986, SIGGRAPH.

[21]  D. Barthélémy,et al.  Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. , 2007, Annals of botany.

[22]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[23]  Xavier Gérard Viennot,et al.  Combinatorial analysis of ramified patterns and computer imagery of trees , 1989, SIGGRAPH.

[24]  Chen Xin.  Simulation of 3D plant evolution based on genetic algorithm , 2009 .