BookDOI
Fuzzy evolutionary computation
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Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components M.A. Lee, H.O. Esbensen, and an Indexed Bibliography of Genetic Algorithms with Fuzzy Logic and Evolutionary Computation.Abstract:
Editor's Preface. Part 1: Fundamentals. 1.1. Evolutionary Algorithms Z. Michalewicz, et al. 1.2. On the Combination of Fuzzy Logic And Evolutionary Computation: A Short Review and Bibliography O. Cordon, et al. 1.3. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components M.A. Lee, H. Esbensen. Part 2: Methodology and Algorithms. 2.1. GA Algorithms in Intelligent Robots T. Fukuda, et al. 2.2. Development of If-Then Rules with the Use of DNA Coding T. Furuhashi. 2.3. Genetic-Algorithm-Based Approaches to Classification Problems H. Ishibuchi, et al. 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems Through Genetic Algorithms M. Sakawa, T. Shibano. 2.5. Multistage Evolutionary Optimization of Fuzzy Systems - Application to Optimal Fuzzy Control J. Kacprzyk. 2.6. Evolutionary Learning in Neural Fuzzy Control Systems D.A. Linkens, H.O. Nyongesa. 2.7. Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach G. Vukovich, J.X. Lee. 2.8. Evolutionary Based Learning of Fuzzy Controllers L. Magdalena, J.R. Velasco. 2.9. GA-Based Generation of Fuzzy Rules O. Nelles. Part 3: Bibliography. 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic J.T. Alander. Subject Index.read more
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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
TL;DR: A MOEA based on SPEA2 (Strength Pareto Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods.
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Ten years of genetic fuzzy systems: current framework and new trends
TL;DR: The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems.
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Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management
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