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Automated ab initio synthesis of complete designs of four patented optical lens systems by means of genetic programming

Published online by Cambridge University Press:  12 June 2008

John R. Koza
Affiliation:
Biomedical Informatics Program, Department of Medicine, Stanford University, Stanford, California, USA
Sameer H. Al-Sakran
Affiliation:
Genetic Programming Inc., Mountain View, California, USA
Lee W. Jones
Affiliation:
Genetic Programming Inc., Mountain View, California, USA

Abstract

This paper describes how genetic programming has been used as an invention machine to automatically synthesize complete designs for four optical lens systems that duplicated the functionality of previously patented lens systems. The automatic synthesis of the complete design is done ab initio, that is, without starting from a preexisting good design and without prespecifying the number of lenses, the topological arrangement of the lenses, or the numerical or nonnumerical parameters associated with any lens. One of the genetically evolved lens systems infringed a previously issued patent, whereas the others were noninfringing novel designs that duplicated (or improved upon) the performance specifications contained in the patents. One of the patents was issued in the 21st century. The designs were created in a substantially similar and routine way, suggesting that the approach described in the paper can be readily applied to other similar problems in the field of optical design. The genetically evolved designs are instances of human-competitive results produced by genetic programming in the field of optical design.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Alander, J.T. (2000). An Indexed Bibliography of Genetic Algorithms in Optics and Image Processing—Draft August 16, 2000, Report 94-1-OPTICS. Vassa, Finland: University of Vaasa, Department of Information Technology and Production Economics.Google Scholar
Al-Sakran, S.H., Koza, J.R., & Jones, L.W. (2005). Automated re-invention of a previously patented optical lens system using genetic programming. Genetic Programming: 8th European Conf., EuroGP 2005, Lecture Notes in Computer Science (Keijzer, M., Tettamanzi, A., Collet, P., van Hemert, J., & Tomassini, M., Eds.), Vol. 3447, pp. 2537. Heidelberg: Springer–Verlag.CrossRefGoogle Scholar
Banzhaf, W., Nordin, P., Keller, R.E., & Francone, F.D. (1998). Genetic Programming—An Introduction. San Francisco, CA/Heidelberg: Morgan Kaufmann/dpunkt.CrossRefGoogle Scholar
Beaulieu, J., Gagné, C., & Parizeau, M. (2002). Lens system design and re-engineering experimentations with genetic algorithms and genetic programming. Proc. 2002 Genetic and Evolutionary Computation Conf.Langdon, W.B., Cantu-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., & Jonoska, N., Eds.), pp. 155162. San Francisco, CA: Morgan Kaufmann.Google Scholar
Comisky, W., Yu, J., & Koza, J. (2000). Automatic synthesis of a wire antenna using genetic programming. Late-Breaking Papers at the 2000 Genetic and Evolutionary Computation Conf., pp. 179186.Google Scholar
Fischer, R.E., & Tadic-Galeb, B. (2000). Optical System Design. New York: McGraw–Hill.Google Scholar
Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.Google Scholar
Jones, L.W., Al-Sakran, S.H., & Koza, J.R. (2005). Automated design of a previously patented aspherical optical lens system by means of genetic programming. In Genetic Programming Theory and Practice III (Yu, G., Worzel, W., & Riolo, R., Eds.), Chap. 3, pp. 3348. New York: Springer.Google Scholar
Keane, M.A., Koza, J.R., & Streeter, M.J. (2005). Apparatus for improved general-purpose PID and non-PID controllers. US Patent 6,847,851. Filed July 12, 2002. Issued January 25, 2005.Google Scholar
Koizumi, N., & Watanabe, N. (2000). Wide-field eyepiece. US Patent 6,069,750. Filed August 17, 1998. Issued May 30, 2000.Google Scholar
Konig, A. (1940). Telescope eyepiece. US Patent 2,206,195. Filed in Germany December 24, 1937. Filed in United States December 14, 1938. Issued July 2, 1940.Google Scholar
Koza, J.R. (1990). Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Technical Report STAN-CS-90-1314, Stanford University, Computer Science Department.Google Scholar
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
Koza, J.R. (1994 a). Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press.Google Scholar
Koza, J.R. (1994 b). Genetic Programming II Videotape: The Next Generation. Cambridge, MA: MIT Press.Google Scholar
Koza, J.R., Al-Sakran, S.H., & Jones, L.W. (2005 a). Automated re-invention of six patented optical lens systems using genetic programming. Proc. Genetic and Evolutionary Computation Conf. (GECCO–2005) (Beyer, H.-G., et al. , Eds.), pp. 19531960. New York: ACM Press.CrossRefGoogle Scholar
Koza, J.R., Al-Sakran, S.H., & Jones, L.W. (2005 b). Cross-domain features of runs of genetic programming used to evolve designs for analog circuits, optical lens systems, controllers, antennas, mechanical systems, and quantum computing circuits. Proc. 2005 NASA/DoD Conf. Evolvable Hardware (Lohn, J., et al. , Eds.), pp. 205212. Los Alamitos, CA: IEEE Computer Society Press.Google Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1996 a). Toward evolution of electronic animals using genetic programming. Artificial Life V: Proc. 5th Int. Workshop on the Synthesis and Simulation of Living Systems (Langton, C.G., & Shimohara, K., Eds.), pp. 327334. Cambridge, MA: MIT Press.Google Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1996 b). Four problems for which a computer program evolved by genetic programming is competitive with human performance. Proc. 1996 IEEE Int. Conf. Evolutionary Computation, pp. 110. New York: IEEE Press.Google Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1996 c). Automated design of both the topology and sizing of analog electrical circuits using genetic programming. Proc. Artificial Intelligence in Design'96 (Gero, J.S., & Sudweeks, F., Eds.), pp. 151170. Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1996 d). Automated WYWIWYG design of both the topology and component values of analog electrical circuits using genetic programming. Genetic Programming 1996: Proc. First Annual Conf. (Koza, J.R., Goldberg, D.E., Fogel, D.B., & Riolo, R.L., Eds.), pp. 123131. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1996 e). Reuse, parameterized reuse, and hierarchical reuse of substructures in evolving electrical circuits using genetic programming. Proc. Int. Conf. Evolvable Systems: From Biology to Hardware (ICES–96), Lecture Notes in Computer Science (Higuchi, T., Iwata, M., & Liu, W., Eds.), Vol. 1259, pp. 312326. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., & Keane, M.A. (1999). Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, CA: Morgan Kaufmann.Google Scholar
Koza, J.R., Bennett, F.H. III, Andre, D., Keane, M.A., & Brave, S. (1999). Genetic Programming III Videotape: Human-Competitive Machine Intelligence. San Francisco, CA: Morgan Kaufmann.Google Scholar
Koza, J.R., Jones, L.W., Keane, M.A., Streeter, M.J., & Al-Sakran, S.H. 2004. Toward automated design of industrial-strength analog circuits by means of genetic programming. In Genetic Programming Theory and Practice II (O'Reilly, U.-M., Riolo, R.L., Yu, G., & Worzel, W., Eds.), pp. 121142. Boston: Kluwer Academic.Google Scholar
Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., & Lanza, G. (2003). Genetic Programming IV: Routine Human-Competitive Machine Intelligence. New York: Kluwer Academic.Google Scholar
Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G., & Fletcher, D. (2003). Genetic IV Video: Routine Human-Competitive Machine Intelligence. New York: Kluwer Academic.Google Scholar
Koza, J.R., Keane, M.A., Yu, J., Bennett, F.H. III, & Mydlowec, W. (2000). Automatic creation of human-competitive programs and controllers by means of genetic programming. Genetic Programming and Evolvable Machines 1, 121164.CrossRefGoogle Scholar
Koza, J.R., & Rice, J.P. (1992). Genetic Programming: The Movie. Cambridge, MA: MIT Press.Google Scholar
Langdon, W.B., & Poli, R. (2002). Foundations of Genetic Programming. New York: Springer–Verlag.CrossRefGoogle Scholar
Moore, G.E. (1996). Can Moore's law continue indefinitely? Computerworld Leadership Series 2(6), 27.Google Scholar
Nagler, A. (1985). Wide angle eyepiece. US Patent 4,525,035. Filed January 5, 1984. Issued January 25, 1985.Google Scholar
Scidmore, W.H. (1968). Wide angle eyepiece. US Patent 3,390,935. Filed August 9, 1965. Issued July 2, 1968.Google Scholar
Smith, W.J. (1992). Modern Lens Design: A Resource Manual. Boston: McGraw–Hill.Google Scholar
Smith, W.J. (2000). Modern Optical Engineering, 3rd ed.New York: McGraw–Hill.Google Scholar
Sterling, T.L., Salmon, J., & Becker, D.J., & Savarese, D.F. (1999). How to Build a Beowulf: A Guide to Implementation and Application of PC Clusters. Cambridge, MA: MIT Press.Google Scholar
Tackaberry, R.B., & Muller, R.M. (1958). Telescope eyepiece system. US Patent 2,829,560. Filed October 15, 1956. Issued April 8, 1958.Google Scholar