Reusch Computational Intelligence, Theory and Applications

International Conference 8th Fuzzy Days in Dortmund, Germany, Sept. 29-Oct. 01, 2004 Proceedings
2005
ISBN: 978-3-540-31182-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 33, 769 Seiten, eBook

Reihe: Advances in Intelligent and Soft Computing

This book constitutes the refereed proceedings of the 8th Dortmund Fuzzy Days, held in Dortmund, Germany, 2004. The Fuzzy-Days conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. All the papers had to undergo a thorough review guaranteeing a solid quality of the programme. The papers are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.

Written for: Engineers, scientists and graduate students in Computational Intelligence

Keywords: Computational Intelligence, Fuzziness, Soft Computing.

Zielgruppe

Research

Autoren/Hrsg.

Reusch, Bernd

Inhaltsverzeichnis

Session Evolutionary Algorithms.- An Evolutionary Algorithm for the Unconstrained Binary Quadratic Problems.- Application of Genetic Algorithms by Means of Pseudo Gradient.- Optimization by Island-Structured Decentralized Particle Swarms.- Directed Mutation by Means of the Skew-Normal Distribution.- Session Rule-Based Fuzzy Inference.- Smooth Extensions of Fuzzy If-Then Rule Bases.- Pre-validation of a Fuzzy Model.- Multiresolution Fuzzy Rule Systems.- Invited Session Data Characterization through Fuzzy Clustering.- Fuzzy Clustering of Macroarray Data.- Fuzzy Clustering: Consistency of Entropy Regularization.- Fuzzy Long Term Forecasting through Machine Learning and Symbolic Representations of Time Series.- Fuzzy Prototypes Based on Typicality Degrees.- Plenary Talk.- The Power of Zadeh’s Protoforms: Towards General Problem Formulations in Fuzzy Multistage Control and Group Decision Making.- Session Fuzzy Control.- Fuzzy Logic Fluid Therapy Control System for Renal Transplantation.- Interpolative Fuzzy Reasoning in Behaviour-Based Control.- Fuzzy Modeling of Offensive Maneuvers in an Air-to-Air Combat.- Invited Session Recent Advances in Theoretical Soft Computing.- Approximation of Fuzzy Functions by Extended Fuzzy Transforms.- Fuzzy Control as a General Interpolation Problem.- Galois Connections with Truth Stressers: Foundations for Formal Concept Analysis of Object-Attribute Data with Fuzzy Attributes.- Fuzzy Transforms in Removing Noise.- Safe Modelling of Fuzzy If-Then Rules.- Perception-Based Logical Deduction.- Invited Session Towards Intelligent Decision Support Systems via Soft Computing.- Fuzzy Object-Oriented Modelling with Metadata Attributes in C#.- Strategies for Decision Making in the Conditions of Intuitionistic Fuzziness.- Fuzzy Linguistic Summaries in Text Categorization for Human-Consistent Document-Driven Decision Support Systems.- An Application of Intuitionistic Fuzzy Relational Databases in Football Match Result Predictions.- Generalized Net Model for Adaptive Electronic Assessment, Using Intuitionistic Fuzzy Estimations.- Session Fuzzy Logic in Decision Support.- Analytic Hierarchy Process Based on Fuzzy Analysis.- A Fuzzy-Ga Hybrid Technique for Optimization of Teaching Sequences Presented in ITSs.- Consistency Conditions for Fuzzy Choice Functions.- Session Applications of Fuzzy Systems.- A Fuzzy Logic Application to Environment Management System: A Case Study for Goksu Streams Water Quality Assesment.- Combination Rule of Normal Degrees on Automated Medical Diagnosis System (AMDS).- Generation of Representative Symptoms Based on Fuzzy Concept Lattices.- Session Connectives.- On the Direct Decomposability of Fuzzy Connectives, Negations and Implications Based on T-Norms and T-Conorms on Product Lattices.- The Cancellation Law for Addition of Fuzzy Intervals.- Generic View On Continuous T-Norms and T-Conorms.- Invited Session Intelligent Techniques for Knowledge Extraction and Management.- Mining Class Hierarchies from XML Data: Representation Techniques.- Generalizing Quantification in Fuzzy Description Logics.- Fuzzy Types: A First Step Towards Lazy Types in the .NET Framework.- Fuzzy Induction via Generalized Annotated Programs.- Evaluating Fuzzy Association Rules on XML Documents.- Plenary Talk.- Ubiquitous Robot.- Session Fuzzy Image Processing.- Combining Fuzzy Logic and Kriging for Image Enhancement.- Optical Quality Control of Coated Steel Sheets Using Fuzzy Grey Scale Correlograms.- Plenary Talk.- Fuzzy Methods in Knowledge Discovery.- Invited Session Evolutionary Algorithms.- Action Games: Evolutive Experiences.- Co-evolving Multilayer Perceptrons Along Training Sets.- Improving Parallel GA Performances by Means of Plagues.- Hybrid Evolutionary Algorithms for Protein Structure Prediction under the HPNX Model.- Invited Session Aggregation Operators.- Quasi-Copulas on Discrete Scales.- Basic Classification of Aggregation Operators and Some Construction Methods.- Homogeneous Aggregation Operators.- 1-Lipschitz Aggregation Operators, Quasi-Copulas and Copulas with Given Opposite Diagonal.- Fuzzy Measures and Choquet Integral on Discrete Spaces.- Session Neural Networks.- Modular Neural Network Applied to Non-Stationary Time Series.- A Feedforward Neural Network based on Multi-Valued Neurons.- Least-Squares Support Vector Machines for Scheduling Transmission in Wireless Networks.- Neural Networks for the Control of Soccer Robots.- Session Neuro-Fuzzy Systems.- Universal Approximator Employing Neo-Fuzzy Neurons.- Combined Learning Algorithm for a Self-Organizing Map with Fuzzy Inference.- Fuzzy/Neural Connection Admission Controller for Multimedia Traffic in Wireless ATM Networks.- Session Fuzzy Mathematics.- Limits of Functional Sequences in the Concept of Nearness Relations.- On the Law of Large Numbers on IFS Events.- An Axiomatic Approach to Cardinalities of IF Sets.- Session Fuzzy Optimization.- Sensitivity Analysis for Fuzzy Shortest Path Problem.- Fuzzy Coloring of Fuzzy Hypergraph.- Nonlinear Optimization with Fuzzy Constraints by Multi-Objective Evolutionary Algorithms.- Session Poster Contributions.- Comparison of Reasoning for Fuzzy Control.- Evolving Scientific Knowledge.- Coding of Chaotic Orbits with Recurrent Fuzzy Systems.- Genetic-Based Tuning of Fuzzy Dempster-Shafer Model.- A Novel Design for Classifying Multi-Field Internet Packets Using Neural Networks.- Modeling Uncertainty in Decision Support Systems for Customer Call Center.- A New GA-Based Real Time Controller for the Classical Cart-Pole Balancing Problem.- Depth Control of Desflurane Anesthesia with an Adaptive Neuro-Fuzzy System.- Ultrasound Intensity and Treatment Time Fuzzy Logic Control System for Low Cost Effective Ultrasound Therapy Devices.

Leseproben

2 The principle of the new evolutionary algorithm. (p. 4)

2.1 The structure of the algorithm.

Hybrid EAs are frequently used for solving combinatorial problems. These methods improve the quality of the descendent solution for example with the application of a local search procedure, SA, or TS. The constitution of these systems corresponds to an extension of an EA: for instance a local search procedure is applied at every step of the EA cycle.

The new EA unlike former hybrid EAs based on a single stage, uses a 2-stage algorithm structure in order to speed up convergence and to produce higher quality results. The first stage is a quick "preparatory" stage that is designated to improve the quality of the initial population. The second stage is a hybrid EA with some special operators.

Let us discuss the 2 EAs (stages) in more detail:

1. The first stage forms some solutions at random and then tries to improve them by randomly generating descendents. The descendent may replace the most similar one of the former solutions.

2. The second stage is a hybrid ES. The algorithm uses two different recombination operations, and concatenated, complex neighbourhood structures for the mutations. The recombination operation is a uniform or single-point recombination or otherwise simple copy-making.

In selecting the parents, priority is given to the best, highest objective/fitness function value: the algorithm selects the fittest solution with 0.5 probability and another solution with 0.5/t probability (where t is the size of the population).

By mutation we applied varying number of bit-flip and a special bit-flip (bit- flip-flop). We form the neighbourhood structure using: some bit-flip-flops + some bit-flips.

The quality of the solutions is improved with a local search procedure. We applied the randomized k-opt local search (Merz and Katayama 2001). Finally in order to keep the diversity of the population we use a filter and a restart procedure. The filter selects only the best of the solutions close to each other, the other ones are deleted.

The restart begins the second stage again, if the fittest solution didn’t change in the last generations. It replaces the weakest solutions with new ones (70% of the population), and it applies the local search procedure on a part of the new individuals.

3 The new algorithm

3.1 The characteristics of the EAs
The main functions and characteristics of the EAs are the following:

Initial population. The same population and individuals are used in all stages. The first individuals of the P population are randomly generated from S. These are the first "solutions".

Fitness function. The algorithm uses the objective function f(x) as fitness function.

Selection operator. In the first stage descendents are randomly selected from S, without the application of any further operators (recombination, mutation). In the second stage the algorithm selects two different parents from the population: the first of them is the most appropriate solution with 0.5 probabilities.