Subakti, M.M.I. (2005) Genetic Simulated Annealing for Null Values Estimating in Generating Weighted Fuzzy Rules from Relational Database Systems


Several methods are proposed to estimate null values in relational database systems. Often the estimated accuracy of the existing methods is not good enough. In this paper, we present an improving method to generate weighted fuzzy rules from relational database systems for estimating null values using Genetic Simulated Annealing (GSA), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights.

After a predefined number of evolutions of the GSA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating null values. The improving method can get a higher average estimated accuracy rate than the existing method (i.e., using Genetic Algorithms). The only constraint of GSA is time. It takes longer time comparing with Genetic Algorithms (GA). We also modified the Equation for the better result comparing with the existing method.


fuzzy sets, genetic simulated annealing, null values, weighted fuzzy rules.