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Open issues in genetic programming

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Abstract

It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first issue appeared of the Genetic Programming & Evolvable Machines journal. In particular, during the past two decades there has been a significant range and volume of development in the theory and application of GP, and in recent years the field has become increasingly applied. There remain a number of significant open issues despite the successful application of GP to a number of challenging real-world problem domains and progress in the development of a theory explaining the behavior and dynamics of GP. These issues must be addressed for GP to realise its full potential and to become a trusted mainstream member of the computational problem solving toolkit. In this paper we outline some of the challenges and open issues that face researchers and practitioners of GP. We hope this overview will stimulate debate, focus the direction of future research to deepen our understanding of GP, and further the development of more powerful problem solving algorithms.

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Notes

  1. Note that this is not scalability in a more narrow sense discussed in Sect. 2.8. Rather it refers to a metalevel of characterization here.

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Acknowledgments

The impetus for this article arose out of the EuroGP 2008 debate on Grand Challenges of Genetic Programming which took place on 27 March 2008 at the Evo* event in Naples, Italy. In particular we thank the two other panel members, Nic McPhee and Riccardo Poli, and also the many members of the audience who participated in the debate. Many of these issues have been raised on multiple occasions at previous (and subsequent) EuroGP debates so this inspired us to put these ideas on paper to open the debate to a wider audience. MO’N acknowledges support of Science Foundation Ireland under Grant No. 08/IN.1/I1868. WB acknowledges support from the Canadian National Science and Engineering Research Council (NSERC) under discovery grant RGPIN 283304-07.

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  1. Complex & Adaptive Systems Lab, School of Computer Science & Informatics, University College Dublin, Dublin, Ireland

    Michael O’Neill

  2. University of Milano-Bicocca, Milan, Italy

    Leonardo Vanneschi

  3. GE Global Research, Niskayuna, NY, USA

    Steven Gustafson

  4. Memorial University of Newfoundland, St. John’s, NL, Canada

    Wolfgang Banzhaf

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O’Neill, M., Vanneschi, L., Gustafson, S. et al. Open issues in genetic programming. Genet Program Evolvable Mach 11, 339–363 (2010). https://doi.org/10.1007/s10710-010-9113-2

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