Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms

Abstract

Abstract-In recent years, some methods have been proposed to estimate null values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, 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 proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.

Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

“…GA is an evolutionary theory technique that employs natural evolution principles to find solutions to natural problems using chromosomes, which represent individual solutions to problems [36,37]. GA has found useful applications in many industrial problems and has been used by numerous authors [38][39][40][41][42][43] in literature for numerous optimization problems; hence, interested readers could consult the references for more insight on the topic. In GA, the arrangement of the discrete constituents of the chromosomes known as genes, give them their unique characteristics; hence, the need for selection, crossover, and mutation in GA to develop new traits of emerging individuals in the solutions of real-world problems.…”