Advances in Evolutionary Computing for System Design by Prof. Lakhmi C. Jain, Shing Chiang Tan (auth.), Prof. Lakhmi

By Prof. Lakhmi C. Jain, Shing Chiang Tan (auth.), Prof. Lakhmi C. Jain, Dr. Vasile Palade, Dipti Srinivasan (eds.)

Evolutionary computing paradigms provide powerful and robust adaptive seek mechanisms for process layout. This booklet contains 13 chapters protecting a large zone of issues in evolutionary computing and purposes including:

    • Introduction to evolutionary computing in procedure design
    • Evolutionary neuro-fuzzy systems
    • Evolution of fuzzy controllers
    • Genetic algorithms for multi-classifier design
    • Evolutionary grooming of traffic
    • Evolutionary particle swarms
    • Fuzzy common sense platforms utilizing genetic algorithms
    • Evolutionary algorithms and immune studying for neural network-based controller design
    • Distributed challenge fixing utilizing evolutionary learning
    • Evolutionary computing inside of grid environment
    • Evolutionary online game thought in instant mesh networks
    • Hybrid multiobjective evolutionary algorithms for the sailor project problem
    • Evolutionary ideas in optimization

This e-book may be important to researchers in clever platforms with curiosity in evolutionary computing, program engineers and approach designers. The ebook can be utilized by scholars and teachers as a sophisticated analyzing fabric for classes on evolutionary computing.

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Extra info for Advances in Evolutionary Computing for System Design

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On Fuzzy Systems FUZZ-IEEE99, Seoul, South Korea, 1802–1806 44 G. Castellano et al. 162. Silva N, Macedo H, Rosa A (1998) Evolutionary fuzzy neural networks automatic design of rule based controllers of nonlinear delayed systems. In Proc. of IEEE World Congress on Computational Intelligence - Fuzzy Systems Proceedings 2:1271–1276 163. Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD Thesis, University of Pittsburgh 164. Srinivas M, Patnaik LM (1991) Learning neural network weights using genetic algorithms Improving performance by search-space reduction.

Sanchez E, Shibata T, Zadeh L (eds) (1997) Genetic Algorithms and Fuzzy Logic Systems. Soft Computing Perspectives. World Scientific, Singapore 153. Saravanan N, Fogel DB (1995) Evolving neural control systems. IEEE Expert 10:23–27 154. Sarkar M, Yegnanarayana B (1997) Evolutionary programming-based probabilistic neural networks construction technique. In: Proc. 1997 IEEE Int. Conf. Neural Networks, pp. 456–461 155. Schwefel HP (1994) On the Evolution of Evolutionary Computation. In: [195]: 116–124 156.

Appl. Intell. 8(1):73–84 145. Rahmoun A, Berrani S (2001) A Genetic-Based Neuro-Fuzzy Generator: NEFGEN, ACS/IEEE International Conference on Computer Systems and Applications, 18–23 146. Rojas I, Gonzalez J, Pomares H, Rojas FJ, Fernandez FJ, Prieto A (2001) Multidimensional and multideme genetic algorithms for the construction of fuzzy systems. Int. J. Approx. Reasoning 26(3):179–210 147. Ross T (1997) Fuzzy Logic with Engineering Applications. McGraw-Hill 148. Roubos H, Setnes M (2001) Compact and transparent fuzzy models through iterative complexity reduction.

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