An inside analysis of a genetic-programming based optimizer

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ideas/Muntes-MuleroZM06,
  author =       "Victor Muntes-Mulero and Calisto Zuzarte and 
                 Volker Markl",
  title =        "An inside analysis of a genetic-programming based
                 optimizer",
  booktitle =    "10th International Database Engineering and
                 Applications Symposium (IDEAS'06)",
  year =         "2006",
  editor =       "Parisa Ghodous and Rose Dieng-Kuntz and 
                 Geilson Loureiro",
  pages =        "249--255",
  address =      "Antibes, France",
  month =        sep # " 18-22",
  publisher =    "IOS Press",
  bibdate =      "2007-01-26",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ideas/ideas2006.html#Muntes-MuleroZM06",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-58603-651-3",
  DOI =          "doi:10.1109/IDEAS.2006.10",
  abstract =     "The use of evolutionary algorithms has been proposed
                 as a powerful random search strategy to solve the join
                 order problem. Specifically, genetic programming used
                 in query optimisation has been proposed as an
                 alternative to the limitations of dynamic programming
                 with large join queries. However, very little is known
                 about the impact and behaviour of the genetic
                 operations used in this type of algorithms.

                 In this paper, we present an analysis that helps us to
                 understand the effect of these operations during the
                 optimization execution. Specifically, we study five
                 different aspects: the age of the members in the
                 population in terms of generations, the number of query
                 execution plans (QEP) discarded without producing new
                 offsprings, the average QEP life time in generations,
                 the efficiency of the genetic operations and the
                 evolution of the best cost. All in all, our analysis
                 allows us to understand the impact of crossovers
                 compared to mutation operations and the dynamically
                 changing effects of these operations.",
  notes =        "Victor Muntes-Mulero, DAMA-UPC, Computer Arch. Dept.
                 Campus Nord. UPC Calisto Zuzarte, IBM Canada Ltd DB2
                 IBM Toronto Lab., Markham, Ontario Volker Markl, IBM
                 Almaden Research Center San Jose, CA",
}

Genetic Programming entries for Victor Muntes-Mulero Calisto Zuzarte Volker Markl

Citations