Fuzzy Rules Generation using Genetic Algorithms with Self-adaptive Selection

Abstract

The definition of the Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. A method for the generation of fuzzy rule bases using genetic algorithm, including a phase of preselection of candidate rules, has been proposed by the authors. The selection of candidate rules uses criteria based on heuristics related to the degree of coverage of the rules. This paper proposes the use of a self-adaptive algorithm for the fitness calculation in the genetic algorithm, as an improvement of the referred method. The algorithm proposed emphasises the usefulness of compact rule bases as a means of transparency enhancement. Some experiment results are presented with a brief discussion of the advantages of the proposal.

Detection of SQL Injection Using a Genetic Fuzzy Classifier System

“…In general, the quality of rules in an FRBS is one of the parameters that favor accuracy, while the number of rules is the parameter that favors transparency, an FRBS with a small number of rules can make the model easily understood by the user. Several approaches in the field of FRBS reduce the FRB size at the expense of accuracy [38]. In this work, the primary objective is to enhance the accuracy; therefore, the numbers of rules are left as they are.…”

Fuzzy Feature Subset Selection Using the Wang & Mendel Method

“…As an alternative approach to deal with the dimensionality problem, in [4,5] the genetic generation of FRBs from a set of candidate rules is proposed. This method, named DoC-based method, preselects rules by a heuristic criteria based on the Degree of Coverage of all possible rules.…”

Genetic generation of fuzzy knowledge bases: new perspectives

“…The DOC-BASED method, proposed in (Cintra and Camargo, 2007a) and optimized in (Cintra et al, 2010a), generates fuzzy rule bases using a predefined and fixed fuzzy data base. The GA is applied to select a set of rules to form the fuzzy rule base of a fuzzy classification system.…”

“…We experimentally evaluated and compared the FCA-BASED method to similar approaches in (Cintra et al, 2012a,b). The FCA-BASED method presents an innovative and improved alternative for the generation of the genetic search space when compared to the DOC-BASED method (Cintra and Camargo, 2007a), previously proposed. The DOC-BASED method uses a process to extract rules to form its genetic search space based on the degree of coverage of the rules.…”

“…Following this generation step, a genetic process is used to select the rule set that defines the final fuzzy rule base. A similar approach, named DOC-BASED, was proposed in (Cintra and Camargo, 2007a), where the pre-selection of candidate rules is based on the degree of coverage of the rules. Furthermore, in order to reduce the computational cost of the extraction process of the candidate rules, as well as for scalability sake, a method that extracts the candidate rules from data, using the formal concept analysis theory, the FCA-BASED method (Cintra et al, 2012c) was proposed.…”