Paper Info
Title
An inside analysis of a genetic-programming based optimizer
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 optimization 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 behavior 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.
Year
DOI
Venue
2006
10.1109/IDEAS.2006.10
IDEAS
Keywords
Field
DocType
different aspect,average qep life time,genetic operation,query optimization,optimization execution,genetic programming,evolutionary algorithm,query execution plan,dynamic programming,best cost,inside analysis,relational databases,random search,genetic algorithms,genetic operator,evolutionary algorithms
Query optimization,Random search,Data mining,Dynamic programming,Population,Relational database,Evolutionary algorithm,Computer science,Theoretical computer science,Genetic programming,Genetic algorithm,Database
Conference
ISSN
ISBN
Citations 
1098-8068
0-7695-2577-6
2
PageRank 
References 
Authors
0.38
13
5
Name
Order
Citations
PageRank
Victor Muntés-Mulero120422.79
josep aguilarsaborit220.38
Josep-Lluis Larriba-Pey324521.70
Calisto Zuzarte426031.97
Volker Markl52245182.37