By Prof. Lakhmi C. Jain, Shing Chiang Tan (auth.), Prof. Lakhmi C. Jain, Dr. Vasile Palade, Dipti Srinivasan (eds.)
Evolutionary computing paradigms supply strong and robust adaptive seek mechanisms for procedure layout. This booklet contains 13 chapters protecting a large sector of themes in evolutionary computing and functions 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 grid environment
- Evolutionary video game thought in instant mesh networks
- Hybrid multiobjective evolutionary algorithms for the sailor task problem
- Evolutionary innovations in optimization
This booklet could be precious to researchers in clever platforms with curiosity in evolutionary computing, program engineers and procedure designers. The ebook is usually utilized by scholars and academics as a sophisticated examining fabric for classes on evolutionary computing.
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Extra resources for Advances in Evolutionary Computing for System Design
8(1):1–29 144. Pujol JCF, Poli R (1998) Evolving the topology and the weights of neural networks using a dual representation. 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.
IEEE Expert 10:16–22 143. Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Comput. 8(1):1–29 144. Pujol JCF, Poli R (1998) Evolving the topology and the weights of neural networks using a dual representation. 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.
K is the degree of fulﬁlment for the k-th rule, for k = 1, . . , K. To better formalise the scheme of the GA-based approach, we denote by F RB(w, K) a Fuzzy Rule Base with parameter vector w and structure size K. That is, with a number of K fuzzy rules. We indicate by F RB(w0 , K0 ) the rule base of the fuzzy model initially derived by neural learning. Firstly, the structure optimisation procedure is applied to F RB(w0 , K0 ) to remove iteratively rules, until the diﬀerence between the accuracy of the reduced model and the accuracy of the initial model drops below a threshold .
Advances in Evolutionary Computing for System Design by Prof. Lakhmi C. Jain, Shing Chiang Tan (auth.), Prof. Lakhmi C. Jain, Dr. Vasile Palade, Dipti Srinivasan (eds.)