The Practical Handbook of Genetic Algorithms: Applications, Second Edition, 2nd Edition (Hardback) book cover

The Practical Handbook of Genetic Algorithms

Applications, Second Edition, 2nd Edition

Edited by Lance D. Chambers

Chapman and Hall/CRC

544 pages | 200 B/W Illus.

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Hardback: 9781584882404
pub: 2000-12-07
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Description

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.

Table of Contents

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

Subject Categories

BISAC Subject Codes/Headings:
COM059000
COMPUTERS / Computer Engineering
MAT003000
MATHEMATICS / Applied
MAT021000
MATHEMATICS / Number Systems