Fuzzy Genetic Algorithm And Applications Pdf

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. The objective of the research explored the development of Fuzzy Systems and Applications Evolutionary Genetic algorithms in the Aviation Industry, and their demonstrated capability of solving different sets of problem emanating from a wide range of application domains affecting the aviation industry. Save to Library. Create Alert. Launch Research Feed.

There has been an increasing public demand for passenger rail service in the recent times leading to a strong focus on the need for effective and efficient use of resources and managing the increasing passenger requirements, service reliability and variability by the railway management. Hence, effective and cost optimised train scheduling based on the dynamic passenger demand is one of the main issues for passenger railway management. Although the passenger railway scheduling problem has received attention in operations research in recent years, there is limited literature investigating the adoption of practical approaches that capitalize on the merits of mathematical modeling and search algorithms for effective cost optimization. This paper develops a hybrid fuzzy logic based genetic algorithm model to solve the multi-objective passenger railway scheduling problem aiming to optimize total operational costs at a satisfactory level of customer service. This hybrid approach integrates genetic algorithm with the fuzzy logic approach which uses the fuzzy controller to determine the crossover rate and mutation rate in genetic algorithm approach in the optimization process. The numerical study demonstrates the improvement of the proposed hybrid approach, and the fuzzy genetic algorithm has demonstrated its effectiveness to generate better results than standard genetic algorithm and other traditional heuristic approaches, such as simulated annealing.

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Odeh, A. Mora, M. Moreno, J. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space.

PDF | Work's purpose is to ascertain the suitability of the fuzzy-genetic algorithm (​FGA) methodology, introduced by two of the authors in previous | Find, read.

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Intelligent Hybrid Systems pp Cite as. This chapter introduces the basic concepts and concrete methodologies of fuzzy systems, neural networks, and genetic algorithms to prepare the readers for the following chapters. Focus is placed on 1 the similarities between the three technologies through the common keyword of nonlinear relationship in a multidimensional space and 2 how to use these technologies at a practical or programming level.

Nowadays, threats of food shortages are happen in Indonesia. Most of crops that are consumed as main food are cereals commodities. Cereals cultivation often experience some problems in determining whether land is suitable or not for the crops.

Al-Mudhafer, Wathiq , and Maytham Alabbas. An Interdisciplinary study for increasing oil recovery has been made in the present paper. The work was achieved by using optimization techniques for determining the optimal future reservoir performance regarding to infill drilling. Adaptive Genetic Algorithm AGA has been adopted in this paper to optimize the count and locations of infill wells.

A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

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Volume 33 Issue 6 Dec. Turn off MathJax Article Contents. Application of fuzzy genetic algorithm in road detection[J]. Journal of Applied Optics, , 33 6 : PDF KB. Application of fuzzy genetic algorithm in road detection.

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PDF | In this paper the integration of Fuzzy Logic and Genetic Algorithms is discussed. Some potencial Genetic Algorithms applications to fuzzy logic | Find​.

Fuzzy Systems and Applications Evolutionary Genetic Algorithms in the Aviation Industry
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