Genetic Algorithm Optimisation of Distributed Database Queries

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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, 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",
• ISBN = "0-7803-4869-9",
• file = "c047.pdf",
• DOI = "doi:10.1109/ICEC.1998.699724",
• 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. Also known as \cite{699724}",

}

Genetic Programming entries for Michael Gregory

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