Abstract
This paper investigates the applicability and feasibility of real-coded GAs for the complete development of fuzzy systems with imbedded constraints; i.e., automatic design of both fuzzy membership functions and rules subject to certain constraints. In this paper, a real-coded GA called RGA is developed, in which chromosomes are represented as real vectors. The constraints associated with the fuzzy systems are explicitly considered and satisfied during the training process of the fuzzy systems by the RGA. The satisfaction of the constraints is done primarily by using tailor-made genetic operators, which are designed based on the coding structure and the required characteristics of the fuzzy systems. We apply the RGA to the design of three fuzzy systems, called fuzzy decision models (FDMs), to model and forecast economic activities in the crude oil tanker sector of maritime transportation. The effects of the RGA's control parameters on the performance of the RGA, including the crossover and mutation rates, multiple mutation, and selection pressure, are investigated in this paper. Our results indicate that the RGA is robust in developing appropriate FDMs with strong modeling and forecasting capability.