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Optimization is one of the dominant research areas in the field of different subjects viz. Mathematics, Computer Science, Business and Economics.In this paper, an effort has been made to optimize a Decision Support System (DSS) query by using the concept of Exhaustive Enumeration Approach, Dynamic Programming, Genetic Algorithm and Entropy based Genetic Algorithm.The results of different query optimization approaches viz. Exhaustive Enumeration (EA), Dynamic Programming (DP), Restricted Exhaustive Enumeration (REA), Simple Genetic Approach (SGA), Entropy Based Restricted Genetic Approach (ERGA) and (HC-ERGA) Havrda- Charvat Entropy Based Restricted Genetic Approach are compared with each other on the basis of Total Costs, Runtime and Quality of Solution.The concept of Havrda-Charvat entropy is used to resolve the low diversity population problem occurs in Genetic Approach. The experimental results reveal that when the problem is scaled up EA, DP and REA is intractable to provide an optimal solution for DSS queries. Independent of the size and complexity of a DSS query, use of entropy with stochastic approach (HC-ERGA) provides an optimal solution in a very short and constant time.Furthermore, the results of HC-ERGA are more optimal than EA, DP, SGA and ERGA by 4.7-15.5%, 4.7-15.5%, 6.9-19.5% and 1-4.6% respectively.

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