The Practical Handbook of Genetic Algorithms: Applications, Second Edition

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ISBN: 9781584882404 | 1584882409
Cover: Hardcover
Copyright: 12/7/2000

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Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this extensively revised, thoroughly updated second edition. This edition allows practitioners to stay up to date on the more recent discoveries in the field, It continues to provide them with material they can use productively in their own endeavors, including computer code available for download from the Internet.

Model Building, Model Testing and Model Fitting

(31)

Uses of Genetic Algorithms

(2)

Optimizing or Improving the Performance of Operations Systems

(1)

Testing and Fitting Quantitative Models

(1)

Maximizing vs. Minimizing

(1)

Purpose of this Chapter

(1)

Quantitative Models

(3)

Parameters

(1)

Revising the Model for Revising the Data?

(1)

Hierarchic or Stepwise Model Building: The Role of Theory

(1)

Significance and Meaningfulness

(2)

Analytical Optimization

(1)

An Example: Linear Regression

(1)

Iterative Hill-Climbing Techniques

(7)

Iterative Incremental Stepping Method

(1)

An Example: Fitting the Continents Together

(2)

Other Hill-Climbing Methods

(1)

The Danger of Entrapment on Local Optima and Saddle Points

(1)

The Application of Genetic Algorithms to Model Fitting

(1)

Assay Continuity in a Gold Prospect

(14)

Description of the Problem

(1)

A Model of Data Continuity

(3)

Fitting the Data to the Model

(1)

The Appropriate Misfit Function

(2)

Fitting Models of One or Two Parameters

(4)

Fitting the Non-homogeneous Model 3

(3)

Conclusion

(3)

Reference

(2)

Compact Fuzzy Models and Classifiers through Model Reduction and Evolutionary Optimization

(30)

Introduction

(2)

Fuzzy Modeling

(4)

The Takagi-Sugeno Fuzzy Model

(1)

Data-Driven Identification by Clustering

(2)

Estimating the Consequent Parameters

(1)

Transparency and Accuracy of Fuzzy Models

(4)

Rule Base Simplification

(1)

Genetic Multi-objective Optimization

(2)

Genetic Algorithms

(3)

Fuzzy Model Representation

(1)

Selection Function

(1)

Genetic Operators

(1)

Crossover Operators

(1)

Mutation Operators

(1)

Constraints

(1)

Examples

(2)

Nonlinear Plant

(2)

Proposed approach

(1)

TS Singleton Model

(3)

TS Linear Model

(7)

Iris Classification Problem

(1)

Solutions in the literature

(1)

Proposed Approach

(4)

Conclusion

(5)

Reference

(5)

On the Application of Reorganization Operators for Solving a Language Recognition Problem

(38)

Introduction

(2)

Performance across a New Problem Set

(1)

Previous Work

(1)

Reorganization Operators

(11)

The Jefferson Benchmark

(3)

MTF

(2)

SFS

(4)

Competition

(1)

The Experimentation

(9)

The Languages

(3)

Specific Considerations for the Language Recognition Problem

(5)

Data Obtained from the Experimentation

(4)

General Evaluation Criteria

(1)

Evaluation

(3)

Machine Size

(1)

Convergence Rates

(2)

Performance of MTF

(1)

Conclusions and Further Directions

(8)

Reference

(6)

Using GA to Optimise the Selection and Scheduling of Road Projects

(36)

Introduction

(1)

Formulation of the Genetic Algorithm

100

(6)

The Objective

100

(1)

The Elements of the Project Schedule

100

(1)

The Genetic Algorithm

100

(6)

Mapping the GA String into a Project Schedule and Computing the Fitness

106

(11)

Data Required

107

(1)

Imposing Constraints

107

(2)

Calculation of Project Benefits

109

(5)

Calculating Trip Generation, Route Choice and Link Loads

114

(3)

Results

117

(15)

Convergence of Solutions to the Problem

117

(2)

The Solutions

119

(3)

Similarity and Dissimilarity of Solutions: Euclidean Distance

122

(10)

Conclusions: Scheduling Interactive Road Projects by GA

132

(3)

Dissimilar Construction Schedules with High and Almost Equal Payoffs

133

(1)

Similar Construction Schedules with Dissimilar Payoffs

133

(1)

References

133

(2)

Decoupled Optimization of Power Electronics Circuits Using Genetic Algorithms

135

(32)

Introduction

135

(2)

Decoupled Regulator Configuration

137

(3)

Optimization Mechanism of GA

139

(1)

Chromosome and Population Structures

139

(1)

Fitness Functions

140

(1)

Fitness Functions for PCS

140

(4)

OF1 for Objective (1)

141

(2)

OF2 for Objective (2)

143

(1)

OF3 for Objective (3)

144

(1)

OF4 for Objective (4)

144

(1)

Fitness function for FN

144

(4)

OF5 for Objective (1)

145

(1)

OF6 and OF8 for Objective (2) and Objective (4)

145

(3)

OF8 of Objective (3)

148

(1)

Steps of Optimization

148

(3)

Design Example

151

(14)

Conclusions

165

(2)

References

165

(2)

Feature Selection and Classification in the Diagnosis of Cervical Cancer

167

(36)

Introduction

167

(2)

Feature Selection

169

(1)

Feature Selection by Genetic Algorithm

170

(4)

GA Encoding Schemes

171

(1)

GAs and Neural Networks

172

(1)

GA Feature Selection Performance

172

(1)

Conclusions

173

(1)

Developing a Neural Genetic Classifier

174

(4)

Algorithm Design Issues

174

(1)

Problem Representation

175

(2)

Objective Function

177

(1)

Selection Strategy

177

(1)

Parameterization

178

(1)

Validation of the Algorithm

178

(8)

The Dataset

178

(1)

Experiments on Two-Dimensional Data

179

(1)

Results of Two-Dimensional Data Experiments

180

(4)

Lessons from Artificial Data

184

(1)

Experiments on a Cell Image Dataset

184

(2)

Parameterization of the GA

186

(3)

Parameterization Experiments

186

(1)

Results of Parameterization Experiments

187

(1)

Selecting the Neural Network Architecture

188

(1)

Experiments with the Cell Image Dataset

189

(14)

Slide-Based vs. Cell-Based Features

189

(5)

Comparison with the Standard Approach

194

(4)

Discusison

198

(1)

References

199

(4)

Algorithms for Multidimensional Scaling

203

(32)

Introduction

203

(6)

Scope of This Chapter

203

(1)

What is Multidimensional Scaling?

204

(4)

Standard Multidimensional Scaling Techniques

208

(1)

Multidimensional Scaling Examined in More Detail

209

(6)

A Simple One-Dimensional Example

209

(2)

More than One Dimension

211

(2)

Using Standard Multidimensional Scaling Methods

213

(2)

A Genetic Algorithm for Multidimensional Scaling

215

(6)

Random Mutation Operators

216

(2)

Crossover Operators

218

(1)

Selection Operators

219

(1)

Design and Use of a Genetic Algorithm for Multidimensional Scaling

219

(2)

Experimental Results

221

(4)

Systematic Projection

221

(1)

Using the Genetic Algorithm

222

(1)

A Hybrid Approach

223

(2)

The Computer Program

225

(7)

The External Model

225

(1)

Definition of Parameters and Variables

226

(1)

The Main Program

227

(1)

Procedures and Functions

228

(3)

Adapting the Program for C or C++

231

(1)

Using the Extended Program

232

(3)

References

233

(2)

Genetic Algorithm-Based Approach for Transportation Optimization Problems

235

(40)

GA-Based Solution Approach for Transport Models

236

(15)

Introduction

236

(1)

GAB Approach for Single-Objective Bilevel Programming Models

236

(8)

GAB Approach for Multi-Objective Bilevel Programming Models

244

(6)

Summary

250

(1)

GAB Calibration Approach for Transport Models

251

(16)

Introduction

251

(1)

Review of TFS

251

(2)

Clibration Measures

253

(3)

GAB Calibration Procedure

256

(1)

Clibration of TFS

257

(1)

Case Study

258

(9)

Summary

267

(1)

Concluding Remarks

267

(8)

References

268

(3)

Appendix I: Notation

271

(4)

Solving Job-Shop Scheduling Problems by Means of Genetic Algorithms

275

(20)

Introduction

275

(1)

The Job-Shop Scheduling Constraint Satisfaction Problem

276

(1)

The Genetic Algorithm

277

(2)

Fitness Refinement

279

(3)

Variable and Value Ordering Heuristics

280

(2)

Heuristic Initial Population

282

(2)

Experimental Results

284

(7)

Conclusons

291

(4)

References

292

(3)

Applying the Implicit Redundant Representation Genetic Algorithm in an Unstructured Problem Domain

295

(46)

Introduction

295

(1)

Motivation for Frame Synthesis Research

296

(1)

Modeling the Conceptual Design Process

296

(1)

Research in Frame Optimization

297

(1)

The Implicit Redundant Representation Genetic Algorithm

297

(2)

Implementation of the IRR GA Algorithm

299

(1)

Suitabilty of the IRR GA in Conceptual Design

299

(1)

The IRR Genotype/Phenotype Representation

299

(4)

Provision of Dynamic Redundancy

301

(1)

Controlling the Level of Redundancy in the IRR GA Initial Population

302

(1)

Applying the IRR GA to Frame Design Synthesis in an Unstructured Domain

303

(20)

Unstructured Design Problem Formulation

303

(1)

IRR GA Genotype/Phenotype Representation for Frame Design Synthesis

304

(7)

Use of Repair Strategies on Frame Design Alternatives

311

(6)

Generation of Horizontal Members in Design Synthesis Alternatives

317

(2)

Specification of Loads on Unstructured Frame design Alternatives

319

(4)

Finite-Element Analysis of Frame Structures

323

(1)

Deletion of Dynamically Allocated Nodal Linked Lists

323

(1)

IRR GA Fitness Evaluation of Frame Design Synthesis Alternatives

323

(8)

Statement of Frame Design Objectives Used as Fitness Functions

323

(2)

Application of Penalty Terms in IRR GA Fitness Evaluation

325

(6)

Discussion of the Genetic Control Operators Used by the IRR GA

331

(3)

Fitness Sharing among Individuals in the Population

331

(1)

Tournament Selection of New Population Individuals

332

(1)

Multiple Point Crossover of Binary Strings

333

(1)

Single-Bit Mutation of Binary Strings

334

(1)

Results of the Implicit Redundant Representation Frame Synthesis Trials

334

(5)

Evolved Design Solutions for the Frame Synthesis Unstructured Domain

335

(1)

Synthesis versus Optimization of Frame Design Solutions Using IRR GA

335

(4)

Conclcuding Remarks

339

(2)

References

339

(2)

How to Handle Constraints with Evolutionary Algorithms

341

(22)

Introduction

342

(1)

Constraint Handling in EAs

342

(3)

Evolutionary CSP Solvers

345

(6)

Heuristic Genetic Operators

345

(1)

Knowledge-Based Fitness and Genetic Operators

346

(1)

Glass-Box Approach

347

(1)

Genetic Local Search

348

(1)

Co-evolutionary Approach

349

(1)

Heuristic-Based Microgenetic Method

350

(1)

Stepwise Adaptation to Weights

350

(1)

Discussion

351

(1)

Assessment of EAs for CSPs

352

(3)

Conclusion

355

(8)

References

356

(7)

An Optimized Fuzzy Logic Controller for Active Power Factor Corrector Using Genetic Algorithm

363

(28)

Introduction

363

(2)

FLC for the Boost Rectifier

365

(6)

Switching Rule for the Switch SW

366

(1)

Fuzzy Logic Controller (FLC)

367

(3)

Defuzzification

370

(1)

Optimization of FLC by the Genetic Algorithm

371

(8)

Structure of the Chromosome

371

(1)

Initialization of Si

371

(4)

Formulation of Multi-objective Fitness Function

375

(1)

Selection of Chromosomes

376

(1)

Crossover and Mutation Operations

376

(2)

Validation of SI: Recovery of Valid Fuzzy Subsets

378

(1)

Illustrative Example

379

(10)

Conclusions

389

(2)

References

389

(2)

Multilevel Fuzzy Process Control Optimized by Genetic Algorithm

391

(52)

Introduction

391

(1)

Intelligent Control

392

(1)

Multilevel Control

393

(6)

Optimal Control Concept

393

(3)

Process Stability during Genetic Algorithm Optimizing

396

(1)

Optimizing Criteria

397

(2)

Opitimizing Aided by Genetic Algorithm

399

(2)

Genetic Algorithm Parameter

399

(2)

Laboratory Cascaded Plant

401

(11)

Multilevel Control Using Genetic Algorithm

412

(7)

Non-coordinated Multilevel Control Using a PID Controller

412

(7)

Fuzzy Multilevel Coordinated Control

419

(15)

Decision Control Table

421

(13)

Conclusions

434

(9)

References

436

(7)

Evolving Neural Networks for Cancer Radiotherapy

443

(41)

Introduction and Chapter Overview

443

(1)

An Introduction to Radiotherapy

444

(11)

Radiation Therapy Treatment Planning (RTP)

444

(1)

Volumes

445

(1)

Treatment Planning

446

(1)

Recent Developments and Areas of Active Research

447

(4)

Treatment Planning

451

(4)

Evolutionary Artificial Neural Networks

455

(8)

Evolving Network Weights

456

(2)

Evolving Network Architectures

458

(2)

Evolving Learning Rules

460

(1)

EPNet

461

(1)

Addition of Virtual Samples

462

(1)

Summary

463

(1)

Radiotherapy Treatment Planning with EANNs

463

(17)

The Backpropogation ANN for Treatment Planning

463

(3)

Development of an EANN

466

(5)

EANN Results

471

(6)

Breast Cancer Treatment Planning

477

(3)

Summary

480

(2)

Discussion and Future Work

482

(2)

Acknowledgments

484

(1)

References

485

(4)

Index

489

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The Practical Handbook of Genetic Algorithms: Applications, Second Edition

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