The Everyday Engineering of Organizational and Engineering Innovation

  • David E. Goldberg


This paper discusses how the routine study of the theory and design of genetic algorithms (GAs) has led to a number of unexpected results. Specifically, the paper considers how GA theory and design has led to (1) the design of genetic algorithms that solve hard problems quickly, reliably, and accurately, (2) a system for collaborative innovation, (3) methods for designing organizations more effectively, (4) GAs based on effective clustering in organizations, and (5) an extensible family of facetwise organizational models.


Genetic Algorithm Team Size Sweet Spot Design Structure Matrix Innovation Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldberg, D. E. (1983). Computer-aided pipeline operation using genetic algorithms and rule learning. Doctoral dissertation, University of Michigan, Ann Arbor.Google Scholar
  2. 2.
    Goldberg, D. E. (2002). The design of innovation: Lessons from and for competent genetic algorithms. Boston, MA: Kluwer Academic.MATHGoogle Scholar
  3. 3.
    takagi, H. (1998). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275–1296.CrossRefGoogle Scholar
  4. 4.
    Kosorukoff, A., & Goldberg, D.E. (2002). Evolutionary computation as a form of organization, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), 965–972.Google Scholar
  5. 5.
    Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.MATHGoogle Scholar
  6. 6.
    Hadamard, J. (1954). The psychology of invention in the mathematical field. New York: Dover.MATHGoogle Scholar
  7. 7.
    De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, Ann Arbor.Google Scholar
  8. 8.
    Goldberg, D. E., Deb, K., & thierens, D. (1993). Toward a better understanding of mixing in genetic algorithms. Journal of the Society of Instrument and Control Engineers, 32(1), 10–16.Google Scholar
  9. 9.
    thierens, D. (1995) Analysis and design of genetic algorithms. Doctoral dissertation, Katholieke Universiteit Leuven, Leuven, Belgium.Google Scholar
  10. 10.
    thierens, D., & Goldberg, D. E. (1993). Mixing in genetic algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms, 38–45.Google Scholar
  11. 11.
    Pelikan, M. and D. E. Goldberg (2003). Hierarchical BOA solves Ising spin glasses and MAXSAt. GECCO-2003: Proceedings of the Genetic and Evolutionary Computation Conference, 1271–1282.Google Scholar
  12. 12.
    Pelikan, M. (2002). Bayesian optimization algorithm: From single level to hierarchy. Doctoral dissertation, University of Illinois, Urbana, IL.Google Scholar
  13. 13.
    Goldberg, D.E., Welge, M., & Llorà, X. (2003). DISCUS: Distributed innovation and scalable collaboration in uncertain settings (IlliGAL technical Report No. 2003017). Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL.Google Scholar
  14. 14.
    Yu, T.-L. Yassine, A., & Goldberg, D. E. (2003). A genetic algorithm for developing modular product architectures. Proceedings of the ASME 2003 International Design Engineering technical Conferences.Google Scholar
  15. 15.
    Yu, T.-L., Goldberg, D. E., Yassine, A. & Chen, Y.-P.(2003). A genetic algorithm design inspired by organizational theory. Proceedings of the Genetic and Evolutionary Computation Conference ( GECCO-2003), 1620–1621Google Scholar
  16. 16.
    Goldberg, D. E. (2004). The entrepreneurial engineer. Unpublished textbook manuscript.Google Scholar
  17. 17.
    Goldberg, D. E., Yu, T.-L., & Yassine, A. (2004). Calculating efficient team size: Balancing deciding and doing as an elementary optimization problem. Unpublished manuscript.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • David E. Goldberg
    • 1
  1. 1.Department of General EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaIllinois

Personalised recommendations