Genetic Algorithms and Innovation

  • David E. Goldberg
Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 7)


Genetic algorithms (GAs) are defined as search procedures based on the mechanics of natural selection and genetics, and we think we know what innovation is—at least in some qualitative sort of way—but what does one have to do with the other? The connection appeared fairly early in my writing on GAs when I used human innovation in my PhD dissertation (Goldberg, 1983) as a metaphor or an intuitive explanation of how such simple mechanisms as those in genetic algorithms might be doing something quite interesting. My aim was to give a plausible explanation of GA power to new readers in an effort to connect with those who might otherwise find the operation of GAs somewhat suspect. I repeated this argument in my earlier book on genetic algorithms (Goldberg, 1989c), and for some readers of that text the argument was temporarily satisfying; for others it was simply maddening, and so the matter has stood. Yet, as my own work on designing increasingly effective genetic algorithms has proceeded, it seemed that the speed and quality of solutions that we were obtaining were far beyond anything I had expected initially. Because of this, and because of my earlier flirtation with innovation, I wondered if perhaps the design of effective GAs was ultimately helping us create first-order computational models of innovation.


Genetic Algorithm Computational Theory Permutation Code Human Innovation Bold Claim 
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.


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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • David E. Goldberg
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUSA

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