Using a genetic algorithm to evolve cellular automata for 2D/3D computational development

ABSTRACT

Form generation or morphogenesis is one of the main stages of both artificial and natural development. This paper provides results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CA) to produce predefined 2D and 3D shapes. The GA worked by evolving the CA rule table and the number of iterations that the model was to run. After the final chromosomes were obtained for all shapes, the CA model was allowed to run starting with a single cell in the middle of the lattice until the allowed number of iterations was reached and a shape was formed. In all cases, mean fitness values of evolved chromosomes were above 80.

REFERENCES

• 1: R. Breukelaar , Th. Bäck, Using a genetic algorithm to evolve behavior in multi dimensional cellular automata: emergence of behavior, Proceedings of the 2005 conference on Genetic and evolutionary computation, June 25-29, 2005, Washington DC, USA [doi>10.1145/1068009.1068024]
• 2: H. de Garis. Artificial embryology and cellular differentiation. In P. J. Bentley, editor, Evolutionary Design by Computers, pages 281--295. Morgan Kaufmann Publishers, Inc., San Francisco, USA, 1999.
• 3: S. Kumar and P. J. Bentley. An introduction to computational development. In S. Kumar and P. J. Bentley, editors, On Growth, Form and Computers, chapter 1, pages 1--44. Academic Press, New York, NY, USA,2003.
• 4: M. Mitchell, J. P. Crutchfield, and R. Das. Evolving cellular automata with genetic algorithms: A review of recent work. In Proceedings of the First International Conference on Evolutionary Computation and its Applications (EvCA'96), 1996., Moscow, Russia, 1996.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms

Keywords:
cellular automata, computational development, genetic algorithms