Decision Making Process of Genetic Algorithm Based Fuzzy Rules Optimization [Excavation Step in Construction Workface]

Abstract

Combination of Fuzzy logic and Genetic Algorithm is used for searching of optimal solutions. To obtain the globally optimal rules, the Fuzzy rulebases are searched by means of Genetic Algorithm. In Fuzzy inference mechanism, GA is used to find the optimal rules. Defuzzified the rules from inference and then the optimal result is produced. This paper introduced the main concept of Fuzzy Logic, Genetic Algorithm and the use of GA and Fuzzy Logic. The input values entered by user are fuzzified with Triangular or Trapezoidal membership function. These memberships’ values are used to find the active rules. These active rules are optimized by GA with various population sizes. Then the optimal rule is defuzzified with Weight Average Method and then the results are described as number of workers and working days. Genetic Algorithms mimic the process of natural selection, creating a number of potentially optimal solutions to some complex search problem. Computer simulations demonstrate that the fuzzy logic system with genetic algorithms processes good robustness.