Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-27T16:35:09.841Z Has data issue: false hasContentIssue false

Using assembly representations to enable evolutionary design of Lego structures

Published online by Cambridge University Press:  07 November 2003

MAXIM PEYSAKHOV
Affiliation:
Department of Computer Science, College of Engineering, Drexel University, Philadelphia, Pennsylvania 19104, USA
WILLIAM C. REGLI
Affiliation:
Department of Computer Science, College of Engineering, Drexel University, Philadelphia, Pennsylvania 19104, USA

Abstract

This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply messy genetic algorithm (GA) optimization techniques to the evolution of assemblies composed of LegoTM structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random, and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.

Type
Research Article
Copyright
2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bentley, P. (1999). Evolutionary Design by Computers. San Francisco, CA: Morgan Kaufmann.
Chase, S. (1996). Representing designs with logic formulations of spatial relations. In Workshop Notes, Visual Reasoning and Interaction in Design, Fourth Int. Conf. Artificial Intelligence in Design.
Coello, C., Christiansen, A., & Aguirre, A. (1997). Automated design of combinational logic circuits using genetic algorithms. Proc. Int. Conf. Artificial Neural Nets and Genetic Algorithms, ICANNGA'97, pp. 335338.
Fraser, A.S. (1957). Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Sciences, 10, 484491.CrossRefGoogle Scholar
Gen, M. & Cheng, R. (1999). Genetic Algorithms and Engineering Optimization. Hoboken, NJ: Wiley.CrossRef
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison–Wesley.
Goldberg, D., Deb, K., Kargupta, H., & Harik, G. (1993) IlliGAL Report No.93004. Rapid, Accurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms. Urbana, IL: University of Illinois.
Goldberg, D., Deb, K., & Korb, B. (1990). Messy genetic algorithms revised: Studies in mixed size and scale. Complex Systems, 4, 415444.Google Scholar
Goldberg, D., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation analysis and first results. Complex Systems, 3, 493530.Google Scholar
Hartley, S. (1998). Concurrent Programming: The Java Programming Language. Oxford, UK: Oxford University Press.
Heisserman, J. & Woodbury, R. (1993). Generating languages of solid models. Proc. Second Symp. on Solid Modeling and Applications '93, pp. 103112.CrossRef
Jakiela, M., Chapman, C., Duda, J., Adewuya, A., & Saitou, K. (2000). Continuum structural topology design with genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 339356.CrossRefGoogle Scholar
Jakiela, M. & Duda, J. (1997). Generation and classification of structural topologies with genetic algorithm speciation. Journal of Mechanical Design, 119(1), 127130.CrossRefGoogle Scholar
Jakiela, M., Wallace, D., & Flowers, W.C. (1996). Design search under probabilistic specifications using genetic algorithms. Computer-Aided Design, 28(5), 405421.CrossRefGoogle Scholar
Jones, E. (1999). Genetic design of antennas and electronic circuits. PhD Thesis. Durham, NC: Duke University.
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., & Osawa, E. (1997). RoboCup: The robot world cup initiative. Proc. First Int. Conf. Autonomous Agents, pp. 340347.CrossRef
Koza, J., Bennett, F., III, Bennett, F., Andre, D., & Keane, M. (1999). Genetic Programming III: Automatic Programming and Automatic Circuit Synthesis. San Francisco, CA: Morgan Kaufmann.
Luke, S. & Spector, L. (1996). Evolving graphs and networks with edge encoding: Preliminary report. Late Breaking Papers at the Genetic Programming 1996 Conf., pp. 117124.
Pollack, J. & Funes, P. (1997). Computer evolution of buildable objects. Fourth European Conf. Artificial Life, pp. 358367.
Pollack, J. & Funes, P. (1998). Evolutionary body building: Adaptive physical designs for robots. Artificial Life, 4, 337357.Google Scholar
Pollack, J., Watson, R., & Ficici, S. (1999). Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In 1999 Congress on Evolutionary Computation (Angeline, P., Michalewicz, Z., Schoenauer, M., Yao, X. & Zalzala, A., Eds.), pp. 335342. Piscataway, NJ: IEEE.
Rosen, D., Dixon, J., & Finger, S. (1994). Conversion of feature-based design representations using graph grammar parsing. ASME Journal of Mechanical Design, 116(3), 785792.CrossRefGoogle Scholar
Schmidt, L. & Cagan, J. (1998). Optimal configuration design: An integrated approach using grammar. Transactions of the ASME, Journal of Mechanical Design, 120(1), 29.CrossRefGoogle Scholar
Schmidt, L., Shetty, H., & Chase, S. (1996). A graph grammar approach for structure synthesis of Mechanisms. Journal of Mechanical Design, 122(4), 371376.CrossRefGoogle Scholar
Schmidt, L., Shi, H., & Kerkar, S. (1999). The “Generation gap”: A CSP approach linking function to form grammar generation. Proc. DETC99, DETC99/DTM-048.
Simms, K. (1994). Evolving virtual creatures. Artificial Life, 4, 2839.CrossRefGoogle Scholar
Sriram, R.D. (1997). Intelligent systems for engineering: A knowledge-based approach. Berlin: Springer Verlag.CrossRef