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Search Strategies for Grammatical Optimization Problems—Alternatives to Grammar-Guided Genetic Programming

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Computational Intelligence and Efficiency in Engineering Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 595))

Abstract

In this chapter, we have a closer look at search strategies for optimization problems, where the structure of valid solutions is defined through a formal grammar. These problems frequently occur in the genetic programming (GP) literature, especially in the context of grammar-guided genetic programming [18]. Even though a lot of progress has been made to extend and improve GP in the last 25 years and many impressive solutions have been produced by GP, the initial goal of an automated programming machine for generating computer programs is still far away and GP is not yet established as a reliable and general method for solving grammatical optimization problems. Instead, many different GP variants have been described and used for solving specific problems. Today the term GP refers to a large set of related algorithms where the commonality mainly is that an evolutionary algorithm is used to produce solutions which often—but not always—represent code that can be executed by a problem specific virtual machine or an interpreter. This code is most frequently represented either as a tree or as a linear chain of instructions. The term genetic programming thus categorizes algorithms based on their approach to solution manipulation. However, the type of problems that is solved using these algorithms is more general. Especially for practitioners, it is often not relevant how a solution has been produced as only the solution itself is relevant. We argue that even though genetic programming is a powerful approach, it might not always be the optimal approach for solving “genetic programming problems” and instead other algorithms might work better for certain problems. Therefore, in this chapter we take a fresh look at those problems, that we in the following call grammatical optimization problems, and discuss various ways for solving such problems. A severely trimmed down extended abstract for this chapter appeared in [14].

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Notes

  1. 1.

    Available at http://dev.heuristiclab.com/GPDL.

  2. 2.

    It should be noted that the ant problem is still a very simple toy-problem but GP is not much better than random search.

  3. 3.

    Genetic programming usually also uses a mutation operator which changes parts of trees randomly to improve diversity of the genetic material. However, in this contribution we focus on the effects of the crossover operator.

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Acknowledgments

The work described in this chapter has been done within the project HOPL—Heuristic Optimization in Production and Logistics and supported by the Austrian Research Promotion Agency (FFG) within the COMET programme.

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Authors and Affiliations

  1. University of Applied Sciences Upper Austria School of Informatics, Communications and Media, Softwarepark 11, 4232, Hagenberg, Austria

    Gabriel Kronberger & Michael Kommenda

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  1. Gabriel Kronberger
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  2. Michael Kommenda
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Correspondence to Gabriel Kronberger .

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Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Grzegorz Borowik

  2. University of Technology, Sydney, New South Wales, Australia

    Zenon Chaczko

  3. University of Applied Sciences Upper Austria, Hagenberg, Austria

    Witold Jacak

  4. Warsaw University of Technology, Warsaw, Poland

    Tadeusz Łuba

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Kronberger, G., Kommenda, M. (2015). Search Strategies for Grammatical Optimization Problems—Alternatives to Grammar-Guided Genetic Programming. In: Borowik, G., Chaczko, Z., Jacak, W., Łuba, T. (eds) Computational Intelligence and Efficiency in Engineering Systems. Studies in Computational Intelligence, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-15720-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-15720-7_7

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