Taras Kowaliw
Peter Grogono
Nawwaf Kharma
ABSTRACT
We explore the use of the developmental environment as a spatial constraint on a model of Artificial Embryogeny, applied to the growth of structural forms. A Deva model is used to translate genotype to phenotype, allowing a Genetic Algorithm to evolve Plane Trusses. Genomes are expressed in one of several developmental environments, and selected using a fitness function favouring stability, height, and distribution of pressure. Positive results are found in nearly all cases, demonstrating that environment can be used as an effective spatial constraint on development. Further experiments take genomes evolved in some environment and transplant them into different environments, or re-grow them at different phenotypic sizes; It is shown that while some genomes are highly specialized for the particular environment in which they evolved, others may be re-used in a different context without significant re-design, retaining the majority of their original utility. This strengthens the notion that growth via Artificial Embryogeny can be resistant to perturbations in environment, and that good designs may be re-used in a variety of contexts.
- A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Springer, 2003. Google Scholar
Digital Library
- P. Funes and J. Pollack. Computer evolution of buildable objects. In P. Husbands and I. Harvey, editors, Fourth European Conference on Artificial Life, pages 358--367, 1997.Google Scholar
- S. Harding and J. Miller. The dead state: A comparison between direct and developmental encodings. In GECCO, 2006.Google Scholar
- P. E. Hotz. Combining developmental processes and their physics in an artificial evolutionary system to evolve shapes. In S. Kumar and P. Bentley, editors, On Growth, Form and Computers, pages 302--318. Elsevier Academic Press, 2003.Google ScholarCross Ref
- P. E. Hotz, G. Gomez, and R. Pfeifer. Evolving the morphology of a neural network for controlling a foveating retina and its test on a real robot. In Artificial Life VIII: 8th Int. Conf. on the Simulation and Synthesis of Living Systems, 2003. Google ScholarDigital Library
- R. Kicinger, T. Arciszewski, and K. A. D. Jong. Evolutionary computation and structural design: a survey of the state of the art. Computers & Structures, 83(23-24):1943--1978, 2005. Google ScholarDigital Library
- T. Kowaliw, P. Grogono, and N. Kharma. Bluenome: A novel developmental model of artificial morphogenesis. In GECCO, 2004.Google ScholarCross Ref
- T. Kowaliw, P. Grogono, and N. Kharma. The evolution of structural design through artificial embryogeny. In Proceedings of the IEEE First International Symposium on Artificial Life, 2007.Google ScholarCross Ref
- S. Kumar and P. Bentley. On Growth, Form and Computers. Elsevier Academic Press, 2003.Google Scholar
- A. Lindenmayer. Mathematical models for cellular interaction in development. Journal of Theoretical Biology, 18:280--315, 1968.Google ScholarCross Ref
- T. H. G. Megson. Structural and Stress Analysis, 2nd Ed. Elsevier Butterworth Heinmann, 2005.Google Scholar
- J. Miller. Evolving a self-repairing, self-regulating, french flag organism. In GECCO, 2004.Google ScholarCross Ref
- P. Prusinkiewicz and A. Lindenmayer. The Algorithmic Beauty of Plants. Springer-Verlag, 1990. Google ScholarDigital Library
- P. Prusinkiewicz and A. Rolland-Lagan. Modeling plant morphogenesis. Current Opinion in Plant Biology, 9:83--88, 2006.Google ScholarCross Ref
- S. D. Rajan. Sizing, shape and topology design optimization of trusses using a genetic algorithm. Journal of Structural Engineering, 121:1480--1487, 1995.Google ScholarCross Ref
- J. Rieffel and J. Pollack. The emergence of ontogenic scaffolding in a stochastic development environment. In GECCO, 2004.Google ScholarCross Ref
- L. Sekanina and M. Bidlo. Evolutionary design of arbitrarily large sorting networks using development. Genetic Programming and Evolvable Machines, 6(3):319--347, 2005. Google ScholarDigital Library
- K. Stoy and R. Nagpal. Self-reconfiguration using directed growth. In Proc. 7th Int. Symp. on Distributed Autonomous Robotic Systems, pages 1--10, 2004.Google Scholar
- A. Turing. The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society B, 237:37--72, 1952.Google ScholarCross Ref
- H. H. West. Analysis of Structures: An Integration of Classical and Modern Methods. John Wi,ley and Sons, 1989.Google Scholar
- Y. Yang and C. K. Soh. Automated optimum design of structures using genetic programming. Computers & Structures, 80(18-19):1537--1546, 2002.Google ScholarCross Ref
Index Terms
- Environment as a spatial constraint on the growth of structural form
Recommendations
Heterochrony and artificial embryogeny: A method for analyzing artificial embryogenies based on developmental dynamics
Artificial embryogenies are an extension to evolutionary algorithms, in which genotypes specify a process to grow phenotypes. This approach has become rather popular recently, with new kinds of embryogenies being increasingly reported in the literature. ...
A hypercube-based encoding for evolving large-scale neural networks
Research in neuroevolution---that is, evolving artificial neural networks (ANNs) through evolutionary algorithms---is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced ...
Generative and developmental systems
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computationIn evolutionary computation it is common for the genotype to map directly to the phenotype such that each gene in the genotype represents a single discrete component of the phenotype. While this convention is widespread, in nature the mapping is not ...
Comments