Query optimization by simulated annealing
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Optimization of large join queries
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Optimization of large join queries: combining heuristics and combinatorial techniques
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
Genetic programming in database query optimization
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Improving quality and convergence of genetic query optimizers
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A novel hybrid algorithm for join ordering problem in database queries
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Analyzing the genetic operations of an evolutionary query optimizer
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Parameterizing a genetic optimizer
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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The increasing number of applications requiring the use of large join queries reinforces the search for good methods to determine the best execution plan. This is especially true, when the large number of joins occurring in a query prevent traditional optimizers from using dynamic programming. In this paper we present the Carquinyoli Genetic Optimizer (CGO). CGO is a sound optimizer based on genetic programming that uses a subset of the cost-model of IBM®DB2®Universal DatabaseTM(DB2 UDB) for selection in order to produce new generations of query plans. Our study shows that CGO is very competitive either as a standalone optimizer or as a fast post-optimizer. In addition, CGO takes into account the inherent characteristics of query plans like their cyclic nature.