Inference Solutions for Natural Language Propositions Involving Fuzzy Quantifiers and Truth Qualifiers in Case of Modifying Fuzzy Subjects by Genetic Algorithms

Abstract

In this paper, we get an inference solutions for natural language propositions involving fuzzy quantifiers and truth qualifiers in case of modifying fuzzy subjects by using genetic algorithms. We apply shift type transformation for fuzzy subjects. We find the inference solutions depend on high level gene number h. The h gets bigger, the tumor of the solutions becomes larger. We use both straight line subjects and S-function subjects. We find no difference between the solutions in straight line subjects and those in S-function subjects.