Genetic Algorithm Optimisation of Distributed Database Queries
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- @InProceedings{gregory:1998:GAoddq,
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author = "Michael Gregory",
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title = "Genetic Algorithm Optimisation of Distributed Database
Queries",
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booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
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year = "1998",
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pages = "271--276",
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address = "Anchorage, Alaska, USA",
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month = "5-9 " # may,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, algorithm
performance,combinatorial optimisation, cost reduction,
distributed relational database query optimisation,
local search phase, multistart, premature convergence,
random search, real-time query optimisation, simulated
annealing, stochastic optimisation techniques, table
reduction, tailored crossover operator, tailored
mutation operator, tree query, tree-structured data
model, distributed databases, mathematical operators,
query processing, real-time systems, relational
databases, software performance evaluation, tree data
structures",
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ISBN = "0-7803-4869-9",
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file = "c047.pdf",
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DOI = "doi:10.1109/ICEC.1998.699724",
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size = "6 pages",
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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.",
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notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence. Also
known as \cite{699724}",
- }
Genetic Programming entries for
Michael Gregory
Citations