Chambers The Practical Handbook of Genetic Algorithms

Applications, Second Edition
2. Auflage 2010
ISBN: 978-1-4200-3556-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 544 Seiten

Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material they can use productively in their own endeavors.

For this edition, the editor again recruited authors at the top of their field and from a cross section of academia and industry, theory and practice. Their contributions detail their own research, new applications, experiment results, and recent advances. Among the applications explored are scheduling problems, optimization, multidimensional scaling, constraint handling, and feature selection and classification.

The science and art of GA programming and application has come a long way in the five years since publication of the bestselling first edition. But there still is a long way to go before its bounds are reached-we are still just scratching the surface of GA applications and refinements. By introducing intriguing new applications, offering extensive lists of code, and reporting advances both subtle and dramatic, The Practical Handbook of Genetic Algorithms is designed to help readers contribute to scratching that surface a bit deeper.

Zielgruppe

Applied mathematicians, computer scientists, electrical engineers, mechanical engineers, and civil engineers

Autoren/Hrsg.

Chambers, Lance D.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik

Inhaltsverzeichnis

MODEL BUILDING, MODEL TESTING, AND MODEL FITTING
Uses of Genetic Algorithms
Quantitative Models
Analytical Optimization
Iterative Hill-Climbing Techniques
Assay Continuity in a Gold Prospect
Conclusions
COMPACT FUZZY MODELS AND CLASSIFIERS THROUGH MODEL REDUCTION AND EVOLUTIONARY OPTIMIZATION
Fuzzy Modeling
Transparency and Accuracy of Fuzzy Models
Genetic Algorithms
Crossover Operators
Examples
TS Singleton Model
TS Linear Model
Conclusion
ON THE APPLICATION OF REORGANIZATION OPERATORS FOR SOLVING A LANGUAGE RECOGNITION PROBLEM
Performance Across a New Problem Set
Reorganization Operators
The Experimentation
Data Obtained from the Experimentation
General Evaluation Criteria
Evaluation
Conclusions and Further Directions
USING GA TO OPTIMIZE THE SELECTION AND SCHEDULING OF ROAD PROJECTS
Introduction
Formulation of the Genetic Algorithm
Mapping the GA String into a Project Schedule and Computing the Fitness
Results
Conclusions: Scheduling Interactive Road Projects by GA
DECOUPLED OPTIMIZATION OF POWER ELECTRONICS CIRCUITS USING GENETIC ALGORITHMS
Introduction
Decoupled Regulator Configuration
Fitness Function for FN
Steps of Optimization
Design Example
Conclusions
FEATURE SELECTION AND CLASSIFICATION IN THE DIAGNOSIS OF CERVICAL CANCER
Introduction
Feature Selection
Feature Selection by Genetic Algorithm
Developing a Neural Genetic Classifier
Validation of the Algorithm
Parameterization of the GA
Experiments with the Cell Image Data Set
ALGORITHMS FOR MULTIDIMENSIONAL SCALING
Introduction
Multidimensional Scaling Examined in more Detail
A Genetic Algorithm for Multidimensional Scaling
Experimental Results
The Computer Program
Using the Extend Program
GENETIC ALGORITHM-BASED APPROACH FOR TRANSPORTATION OPTIMIZATION PROBLEMS
GAs-Based Solution Approach for Transport Models
GAs-Based Calibration Approach for Transport Models
Concluding Remarks
SOLVING JOB-SHOP SCHEDULING PROBLEMS BY MEANS OF GENETIC ALGORITHMS
Introduction
The Job Shop Scheduling Constraint Satisfaction Problem
The Genetic Algorithm
Fitness Refinement
Heuristic Initial Population
Experimental Results
Conclusions
APPLYING THE IMPLICIT REDUNDANT REPRESENTATION GENETIC ALGORITHM IN AN UNSTRUCTURED PROBLEM DOMAIN
Introduction
Motivation for Frame Synthesis Research Notes in Mathematics series The Implicit Redundant Representation of Genetic Algorithm
The IRR Genotype/Phenotype Representation
Applying the IRR GA to Frame Design Synthesis in an Unstructured Domain
IRR GA Fitness Evaluation of Frame Design Synthesis Alternatives
Discussion of the Genetic Control Operators Used by the IRR GA
Results of the Implicit Redundant Representation Frame Synthesis Trials
HOW TO HANDLE CONSTRAINTS WITH EVOLUTIONARY ALGORITHMS
Introduction
Constraints Handling in EAs
Evolutionary CSP Solvers
Discussion
Assessment of Eas for CSPs
Conclusion
AN OPTIMIZED FUZZY LOGIC CONTROLLER FOR ACTIVE POWER FACTOR CORRECTOR USING GENETIC ALGORITHM
Introduction
FLC for the Boost Rectifier
Optimization of FLC by the Genetic Algorithm
Illustrative Example
Conclusions
MULTILEVEL FUZZY PROCESS CONTROL OPTIMIZED BY GENETIC ALGORITHM
Introduction
Intelligent Control
Multilevel Control
Optimizing Aided by Genetic Algorithm
Laboratory Cascaded Plant
Multilevel Control using Genetic Algorithm
Fuzzy Multilevel Coordinated Control
Conclusions
Evolving Neural Networks for Cancer Radiotherapy
EVOLVING NEURAL NETWORKS FOR CANCER RADIOTHERAPY
Introduction and Chapter Overview
An Introduction to Radiotherapy
Evolutionary Artificial Neural Networks
Radiotherapy Treatment Planning with EANNs
Summary
Discussion and Future Work