- 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",
