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The Challenge of Producing Human-Competitive Results by Means of Genetic and Evolutionary Computation

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Part of the book series: Genetic Algorithms and Evolutionary Computation ((GENA,volume 11))

Abstract

Human-competitive results include those equivalent to new scientific results published in peer-reviewed scientific journals, solutions to long-standing or indisputably difficult problems, patented inventions, and results that tie or beat human contestants in regulated competitions. We argue that the pursuit of human-competitive results is not only a worthy goal in itself, but a useful compass for guiding the future growth of the field. We say this for reasons of utility, objectivity, complexity, and interminability. We believe that the continuing generation of evermore important human-competitive results relies on progress in three areas of research: multiobjective optimization, parallel computing, and the development and perfection of competent genetic and evolutionary search methods. Addressing the characteristics of human-competitive problems is one way to expand the theoretical underpinnings of the field of genetic and evolutionary computation.

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References

  • Cantu-Paz, E. (2000). Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Goldberg, D. E. (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston.

    Google Scholar 

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI. Second edition. Cambridge, MA. The MIT Press 1992.

    Google Scholar 

  • Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.

    Google Scholar 

  • Koza, John R. (1993). Hierarchical automatic function definition in genetic programming. In Whitley, D., editor, Foundations of Genetic Algorithms, volume 2, pages 297–318. Morgan Kaufmann Publishers, San Mateo, CA.

    Google Scholar 

  • Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA.

    Google Scholar 

  • Koza, John R., Bennett III, Forrest, H., Andre, David, and Keane, Martin A. (1999). Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  • Koza, John R., Keane, M. A., Streeter, M. J., Mydlowec, W., Yu, J., and Lanza, G. (2003). Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers.

    Google Scholar 

  • Samuel, A. L. (1983). AI: Where it has been and where it is going. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pages 1152–1157, Los Altos, CA. Morgan Kaufmann.

    Google Scholar 

  • Turing, A. M. (1948). Intelligent machinery. Reprinted in Ince, D. C. (editor). 1992. Mechanical Intelligence: Collected Works of A. M. Turing. Amsterdam: North Holland. Pages 107–127.

    Google Scholar 

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59:433–460. Reprinted in Ince, D. C. (editor). 1992. Mechanical Intelligence: Collected Works of A. M. Turing. Amsterdam: North Holland. Pages 133–160.

    MathSciNet  Google Scholar 

  • Zitzler, E., Deb, K., Lothar, T., Coello, C. A., and David, C., editors (2001). Evolutionary Multi-Criterion Optimization, First International Conference, EMO 2001, Zurich, Switzerland. Lecture Notes in Computer Science. Volume 1993, Springer-Verlag: Berlin, Germany.

    Google Scholar 

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© 2004 Kluwer Academic Publishers

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Koza, J.R., Streeter, M.J., Keane, M.A. (2004). The Challenge of Producing Human-Competitive Results by Means of Genetic and Evolutionary Computation. In: Menon, A. (eds) Frontiers of Evolutionary Computation. Genetic Algorithms and Evolutionary Computation, vol 11. Springer, Boston, MA. https://doi.org/10.1007/1-4020-7782-3_9

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  • DOI: https://doi.org/10.1007/1-4020-7782-3_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7524-7

  • Online ISBN: 978-1-4020-7782-1

  • eBook Packages: Springer Book Archive

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