Hybrid Greedy and Genetic Algorithms for Optimization of Relational Data Warehouses

G. Velinov (FYROM), D. Gligoroski (Norway), and M. Kon-Popovska (FYROM)


Relational Data Warehouses, Greedy, Genetic Algorithm.


In this paper we present two novel algorithms for gener alized problem of selection of optimal set of views, their optimal vertical fragmentation and their optimal set of in dexes. The algorithms are hybrid, i.e. they are combination of Greedy and Genetic Algorithm. We present our exper imental results and show that our algorithms significantly improve the efficiency of the optimization process for dif ferent input parameters. The results show that those algo rithms outperforms Stochastic Ranking evolutionary (Ge netic) Algorithm - SRGA by 60% - 280% in the speed of finding optimal (or near optimal) solutions.

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