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Grammar-based Genetic Programming: a survey

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Abstract

Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.

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Notes

  1. Reproduced under the Creative Commons Licence; available from https://sc.snu.ac.kr/sclab/doku.php?id=commons.

  2. Recombinations in biological systems are usually homologous—the genetic materials are not exchanged completely randomly, but between genes with similar function [81]. By analogy, we term crossovers exchanging subtrees rooted at the same symbols “homologous crossovers”.

  3. While there may be substantial differences between EDA and ACO in general, in their application to GP search spaces, the differences have been largely a matter of terminology.

  4. While the ideas here are due to Ratle and Sebag, we use an equivalent representation closer to the logic grammars of Wong and Leung.

  5. Actually, Ratle and Sebag also impose reasonable bounds on the allowable dimensionality of sub-expressions—also readily expressible in logic grammars—but these are distractions to our illustrative purposes here.

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Acknowledgments

The authors thank Kwong Sak Leung, Man Leung Wong and Brian Ross for insightful discussions that helped to form their perspectives on grammar-based GP, Kee Siong Ng for his suggestions at the final stage of editing. Thanks are also due to the anonymous referees, who helped us to shape the discussion more comprehensibly. Seoul National University Institute for Computer Technology provided some of the research facilities for this study, which was also supported by a Korea Research Foundation Grant funded by the Korean Government (KRF-2008-313-D00943). MO’N thanks Science Foundation Ireland for support under Grant No. 08\IN.1\I1868. NXH was partly funded by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01.14.09 for this work.

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Authors and Affiliations

  1. Structural Complexity Lab, School of Computer Science and Engineering, Seoul National University, Seoul, Korea

    Robert I. McKay

  2. Department of Computer Science, Le Quy Don University, Hanoi, Vietnam

    Nguyen Xuan Hoai

  3. Department of Information Science, University of Otago, Dunedin, NZ, New Zealand

    Peter Alexander Whigham

  4. Medicare Australia, Canberra, Australia

    Yin Shan

  5. Complex and Adaptive Systems Lab, School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Michael O’Neill

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  1. Robert I. McKay
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  2. Nguyen Xuan Hoai
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  5. Michael O’Neill
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Correspondence to Robert I. McKay.

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McKay, R.I., Hoai, N.X., Whigham, P.A. et al. Grammar-based Genetic Programming: a survey. Genet Program Evolvable Mach 11, 365–396 (2010). https://doi.org/10.1007/s10710-010-9109-y

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