Engineering Evolutionary Intelligent Systems
Evolutionary design of intelligent systems is gaining much popularity due to its capabilities in handling several real world problems involving optimization, complexity, noisy and non-stationary environment, imprecision, uncertainty and vagueness. This edited volume 'Engineering Evolutionary In...
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| Other Authors: | , , |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edition: | 1st ed. 2008. |
| Series: | Studies in Computational Intelligence,
82 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-540-75396-4 |
Table of Contents:
- Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews
- Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures
- Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design
- Evolution of Inductive Self-organizing Networks
- Recursive Pattern based Hybrid Supervised Training
- Enhancing Recursive Supervised Learning Using Clustering and Combinatorial Optimization (RSL-CC)
- Evolutionary Approaches to Rule Extraction from Neural Networks
- Cluster-wise Design of Takagi and Sugeno Approach of Fuzzy Logic Controller
- Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization
- A New Genetic Approach for Neural Network Design
- A Grammatical Genetic Programming Representation for Radial Basis Function Networks
- A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth
- On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms
- A Hybrid Cellular Genetic Algorithm for the Capacitated Vehicle Routing Problem
- Particle Swarm Optimization with Mutation for High Dimensional Problems.