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Dynamic Programming Algorithm vs. Genetic Algorithm: Which is Faster?

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Research and Development in Intelligent Systems XXVII (SGAI 2010)

Abstract

The article compares two different approaches for the optimization problem of large join queries (LJQs). Almost all commercial database systems use a form of the dynamic programming algorithm to solve the ordering of join operations for large join queries, i.e. joins with more than dozen join operations. The property of the dynamic programming algorithm is that the execution time increases significantly in the case, where the number of join operations in a query is large. Genetic algorithms (GAs), as a data mining technique, have been shown as a promising technique in solving the ordering of join operations in LJQs. Using the existing implementation of GA, we compare the dynamic programming algorithm implemented in commercial database systems with the corresponding GA module. Our results show that the use of a genetic algorithm is a better solution for optimization of large join queries, i.e., that such a technique outperforms the implementations of the dynamic programming algorithm in conventional query optimization components for very large join queries.

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Authors and Affiliations

  1. University of Applied Sciences, Rosenheim, Germany

    Dušan Petković

Authors
  1. Dušan Petković
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Correspondence to Dušan Petković .

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Editors and Affiliations

  1. Dept. Computer Science and, Software Engineering, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , Faculty of Technology, De Montford University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Adrian Hopgood

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Petković, D. (2011). Dynamic Programming Algorithm vs. Genetic Algorithm: Which is Faster?. In: Bramer, M., Petridis, M., Hopgood, A. (eds) Research and Development in Intelligent Systems XXVII. SGAI 2010. Springer, London. https://doi.org/10.1007/978-0-85729-130-1_36

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  • DOI: https://doi.org/10.1007/978-0-85729-130-1_36

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-129-5

  • Online ISBN: 978-0-85729-130-1

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