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In the area of computational intelligence as like Artificial Neural Networks (ANNs) or Fuzzy logic have been used for the construction of an effective and reliable system in order to solve a real world problem where appropriate outcome along with certainty as well as precision are highly required. In this article, we present an integrated approach based on a fast elitist non-dominated sorting genetic...
This paper aims at the Genetic Algorithm (GA's) based tuning of fuzzy logic controller (FLC). A two-step approach is proposed to tune a fuzzy logic controller using genetic algorithm. Moreover, it has been tried to develop a stepwise method to tune a fuzzy logic controller with GA in less number of generations. Special attention has been given to the learning of knowledge base which can be used for...
This paper proposes a new approach based on Quantum Evolutionary Algorithm (QEA) for effective selection and definition of fuzzy if-then rules to design Fuzzy Logic Controllers (FLCs). The majority of works done on designing FLCs were based on knowledgebase derived from imprecise heuristic knowledge of experienced operators or persons but they were difficult and time consuming to evaluate. The proposed...
Fuzzy Logic Controllers (FLCS) are rule-based system that successfully incorporate the flexibility of human-decision making by means of the use of fuzzy set theory. This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. A three-stage evolution framework that uses Genetic Programming (GP) and Genetic Algorithms (GAS) evolves...
Michigan-style genetic algorithms are usually used for learning fuzzy classification rules from numerical examples. In these approaches, each rule is encoded as a chromosome, and then builds up the classification rule set by these chromosomes. So the fitness value can only assign to a single rule rather than a whole rule set. This makes some chromosomes characterized by the minority of instances may...
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