Genetic Algorithm Optimisation of Distributed Database Queries   [GA]

by

Gregory, M.

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Info: Proceedings of the 1998 IEEE World Congress on Computational Intelligence (Conference proceedings), 1998, p. 271-276
Keywords:genetic algorithms, genetic programming
Abstract:
Distributed relational database query optimisation [RDQ] is a combinatorial optimisation problem. [CO] This paper reports on an initial investigation into the potential for a genetic algorithm (GA) to [GA] optimise distributed queries. A genetic algorithm [GA] is developed and its performance compared with alternative stochastic optimisation techniques: random search, [RS] multistart, and simulated annealing. [SA] The problem of fully reducing all tables in a tree query is used to compare the techniques. For this problem, evaluating the fitness function [FF] is an expensive operation. The proposed GA uses a tree-structured data model with tailored crossover and mutation operators that avoid the need to fully re-evaluate the fitness function [FF] for new solutions. Query optimisation is a task that must be performed in real-time. A technique is required that performs well at the start of a search, but avoids the problem of premature convergence. [PC] The proposed GA uses a local search phase to [LS] deliver the required real-time performance. Experiments show that the proposed GA can perform better than the alternative techniques tested. The potential for a GA to deliver valuable distributed query processing cost reductions is demonstrated.
Notes:
ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence
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BibTex:
@InProceedings{gregory:1998:GAoddq,
  author =       "Michael Gregory",
  title =        "Genetic Algorithm Optimisation of Distributed Database
                 Queries",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "271--276",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c047.pdf",
  size =         "6 pages",
  abstract =     "Distributed relational database query optimisation is
                 a combinatorial optimisation problem. This paper
                 reports on an initial investigation into the potential
                 for a genetic algorithm (GA) to optimise distributed
                 queries. A genetic algorithm is developed and its
                 performance compared with alternative stochastic
                 optimisation techniques: random search, multistart, and
                 simulated annealing. The problem of fully reducing all
                 tables in a tree query is used to compare the
                 techniques. For this problem, evaluating the fitness
                 function is an expensive operation. The proposed GA
                 uses a tree-structured data model with tailored
                 crossover and mutation operators that avoid the need to
                 fully re-evaluate the fitness function for new
                 solutions. Query optimisation is a task that must be
                 performed in real-time. A technique is required that
                 performs well at the start of a search, but avoids the
                 problem of premature convergence. The proposed GA uses
                 a local search phase to deliver the required real-time
                 performance. Experiments show that the proposed GA can
                 perform better than the alternative techniques tested.
                 The potential for a GA to deliver valuable distributed
                 query processing cost reductions is demonstrated.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}