Human-Competitive Evolvable Hardware Created by Means of Genetic Programming

  • John R. Koza
  • Martin A. Keane
  • Matthew J. Streeter
  • Sameer H. Al-Sakran
  • Lee W. Jones
Part of the Genetic and Evolutionary Computation book series (GEVO)


Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming is an extension of the idea of the genetic algorithm into the arena of computer programs. Genetic programming uses the Darwinian principle of natural selection and analogs of recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology to progressively breed, over a series of many generations, an improved population of candidate solutions to a problem. Many human-competitive results have been produced using the genetic programming technique, including the automated reinvention of previously patented inventions. This chapter concentrates on the automatic synthesis of six 21st century patented analog electrical circuits by means of genetic programming. The automatic synthesis of analog circuits is done “from scratch”—that is, without starting from a preexisting good design and without prespecifying the circuit’s topology or number or sizing of components. This chapter also briefly summarizes some examples of the automatic synthesis of other types of complex structures by means of genetic programming.

Key words

evolvable hardware analog electrical circuits genetic programming automated design developmental process reinvention of previously patented entity human-competitive result 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Al-Sakran, Sameer H., John R. Koza, and Lee W. Jones. 2005. Automated re-invention of a previously patented optical lens system using genetic programming. In Keijzer, M., et al. (Eds.). Genetic Programming: 8th European Conference, EuroGP 2005, Lausanne, Switzerland, March 30–April 1, 2005, Proceedings, Lecture Notes in Computer Science, 3447, 25–37. Heidelberg: Springer-Verlag.Google Scholar
  2. Åström, Karl J. and Tore Hägglund. 1995. PID Controllers: Theory, Design, and Tuning. Second Edition. Research Triangle Park, NC: Instrument Society of America.Google Scholar
  3. Aytur, Turgut Sefket. 2000. Integrated Circuit with Variable Capacitor. U.S. patent 6,013,958. Filed July 23, 1998. Issued January 11, 2000.Google Scholar
  4. Banzhaf, Wolfgang, et al. 1998. Genetic Programming — An Introduction. San Francisco, CA: Morgan Kaufinann and Heidelberg: dpunkt.Google Scholar
  5. Cho, Sung-Bae, Hoai Xuan Nguyen, and Yin Shan (Eds.). 2003. Proceedings of the First Asian-Pacific Workshop on Genetic Programming. Scholar
  6. Cipriani, Stefano and Anthony A. Takeshian. 2000. Compact cubic function generator. U. S. patent 6,160,427. Filed September 4, 1998. Issued December 12, 2000.Google Scholar
  7. Comisky, William, Jessen Yu, and John Koza. 2000. Automatic synthesis of a wire antenna using genetic programming. Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, Las Vegas, Nevada. 179–186.Google Scholar
  8. Daun-Lindberg, Timothy Charles, and Michael Lee Miller. 2000. Low Voltage High-Current Electronic Load. U. S. patent 6,211,726. Filed June 28, 1999. Issued April 3, 2001.Google Scholar
  9. Deb, Kalyanmoy, et al. (Eds.). 2004. Genetic and Evolutionary Computation-GECCO 2004: Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 2004. Proceedings, Part I, Lecture Notes in Computer Science 3102. Berlin: Springer.Google Scholar
  10. Grimbleby, J. B. 1995. Automatic analogue network synthesis using genetic algorithms. In Proceedings of the First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). 53–58. London: Institution of Electrical Engineers.CrossRefGoogle Scholar
  11. Gruau, Frederic. 1992. Cellular Encoding of Genetic Neural Networks. Technical report 92-21. Laboratoire de l’Informatique du Parallélisme. Ecole Normale Supérieure de Lyon. May 1992.Google Scholar
  12. Higuchi, Tetsuya, et al. 1993a. Evolving hardware with genetic learning: A first step towards building a Darwin machine. In Meyer, Jean-Arcady, Herbert L. Roitblat and Stewart W. Wilson (Eds.). From Animals to Animals 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, 417–424. Cambridge, MA: The MIT Press.Google Scholar
  13. Higuchi, Tetsuya, et al. 1993b. Evolvable Hardware-Genetic-Based Generation of Electric Circuitry at Gate and Hardware Description Language (HDL) Levels. Electrotechnical Laboratory technical report 93-4. Tsukuba, Japan: Electrotechnical Laboratory.Google Scholar
  14. Holland, John H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press. Second edition. Cambridge, MA: The MIT Press, 1992.Google Scholar
  15. Ikeuchi, Akira and Naoshi Tokuda. 2000. Voltage-Current Conversion Circuit. U. S. patent 6,166,529. Filed February 24, 2000 in U. S. Issued December 26, 2000 in U. S. Filed March 10, 1999 in Japan.Google Scholar
  16. Keane, Martin A., John R. Koza, and Matthew J. Streeter, 2005. Apparatus for Improved General-Purpose PID and Non-PID Controllers. U. S. Patent 6,847,851. Filed July 12, 2002. Issued January 25, 2005.Google Scholar
  17. Keijzer, Maarten, et al. (Eds.). Genetic Programming: 8th European Conference, EuroGP 2005, Lausanne, Switzerland, March 30–April 1, 2005, Proceedings, Lecture Notes in Computer Science 3447. Heidelberg: Springer-Verlag.Google Scholar
  18. Kitano, Hiroaki. 1990. “Designing neural networks using genetic algorithms with graph generation system.” Complex Systems, 4 (1990), 461–476.zbMATHGoogle Scholar
  19. Koza, John R. 1990. Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Stanford University Computer Science Dept. technical report STAN-CS-90-1314. June 1990.Google Scholar
  20. Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.Google Scholar
  21. Koza, John R. 1993. Discovery of rewrite rules in Lindenmayer systems and state transition rules in cellular automata via genetic programming. Symposium on Pattern Formation (SPF-93), Claremont, California. February 13, 1993.Google Scholar
  22. Koza, John R. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press.Google Scholar
  23. Koza, John R., Sameer H. Al-Sakran and Lee W. Jones. 2005. Automated re-invention of six patented optical lens systems using genetic programming. In Beyer, H.-G., et al. (Eds.). Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2005. 1953–1960. New York, NY: ACM Press.CrossRefGoogle Scholar
  24. Koza, John R., et al. 1996a. Automated design of both the topology and sizing of analog electrical circuits using genetic programming. In Gero, John S. and Fay Sudweeks (Eds.). Artificial Intelligence in Design’ 96. 151–170. Dordrecht: Kluwer Academic Publishers.Google Scholar
  25. Koza, John R., et al. 1996b. Reuse, parameterized reuse, and hierarchical reuse of substructures in evolving electrical circuits using genetic programming. In Higuchi, Tetsuya, Masaya Iwata, and Weixin Liu (Eds.). Proceedings of International Conference on Evolvable Systems: From Biology to Hardware (ICES-96), Lecture Notes in Computer Science, Volume 1259. 312–326. Berlin: Springer-Verlag.Google Scholar
  26. Koza, John R., et al. 1999. Genetic Programming III: Darwinian Invention and Problem Solving. San Francisco, CA: Morgan Kaufmann.Google Scholar
  27. Koza, John R., et al. 2003. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers.Google Scholar
  28. Kruiskamp, Marinum Wilhelmus and Domine Leenaerts. 1995. DARWIN: CMOS opamp synthesis by means of a genetic algorithm. In Proceedings of the 32nd Design Automation Conference, 433–438. New York, NY: Association for Computing Machinery.Google Scholar
  29. Langdon, William B. and Riccardo Poli. 2002. Foundations of Genetic Programming. Springer-Verlag.Google Scholar
  30. Lee, Sang Gug. 2001. Law Voltage Balun Circuit. U. S. patent 6,265,908. Filed December 15, 1999. Issued July 24, 2001.Google Scholar
  31. Lipson, Hod. 2004. How to draw a straight line using a GP: Benchmarking evolutionary design against 19th century kinematic synthesis. In Keijzer, Maarten (Ed.). Genetic and Evolutionary Conference 2005 Late-Breaking Papers. CD ROM. Seattle, WA: International Society for Genetic and Evolutionary Computation.Google Scholar
  32. Lohn, Jason D., Greg S. Hornby, and Derek S. Linden. 2004. An evolved antenna for deployment on NASA’s Space Technology 5 Mission. In O’Reilly, Una-May, et al. (Eds.). Genetic Programming Theory and Practice II, Chapter 18. Boston: Kluwer Academic Publishers.Google Scholar
  33. O’Reilly, Una-May, et al. (Eds.). 2004. Genetic Programming Theory and Practice II. Boston: Kluwer Academic Publishers, 121–142.Google Scholar
  34. Quarles, Thomas, et al. 1994. SPICE 3 Version 3F5 User’s Manual. Department of Electrical Engineering and Computer Science, University of California. Berkeley, CA. March 1994.Google Scholar
  35. Smith, Warren J. 2000. Modern Optical Engineering. 3 rd edition. New York: McGraw-Hill.Google Scholar
  36. Spector, Lee. 2004. Automatic Quantum Computer Programming: A Genetic Programming Approach. Boston: Kluwer Academic Publishers.Google Scholar
  37. Thompson, Adrian. 1996. Silicon evolution. In Koza, John R., et al. (Eds.). 1996. Genetic Programming 1996: Proceedings of the First Annual Conference, July 28–31, 1996, Stanford University, 444–452. Cambridge, MA: MIT Press.Google Scholar
  38. Wilson, Stewart. W. 1987. The genetic algorithm and biological development. In Grefenstette, John J. (Ed.). Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, 247–251. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  39. Ziegler, J. G. and N. B. Nichols. 1942. “Optimum settings for automatic controllers.” Transactions of ASME, (64), 759–768.Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2006

Authors and Affiliations

  • John R. Koza
    • 1
  • Martin A. Keane
    • 2
  • Matthew J. Streeter
    • 3
  • Sameer H. Al-Sakran
    • 4
  • Lee W. Jones
    • 4
  1. 1.Stanford UniversityStanford
  2. 2.Econometrics Inc.Chicago
  3. 3.Carnegie Mellon UniversityPittsburgh
  4. 4.Genetic Programming Inc.Mountain View

Personalised recommendations