Evolutionary Algorithms in Engineering and Computer Science Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications

, by Miettinen, K.; Neittaanmä; ki, Pekka; Mä; kelä; , M. M.; Périaux, Jacques

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ISBN: 9780471999027 | 0471999024
Cover: Hardcover
Copyright: 7/9/1999

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Evolutionary Algorithms in Engineering and Computer Science Edited by K. Miettinen, University of Jyväskylä, Finland M. M. Mäkelä, University of Jyväskylä, Finland P. Neittaanmäki, University of Jyväskylä, Finland J. Périaux, Dassault Aviation, France What is Evolutionary Computing? Based on the genetic message encoded in DNA, and digitalized algorithms inspired by the Darwinian framework of evolution by natural selection, Evolutionary Computing is one of the most important information technologies of our times. Evolutionary algorithms encompass all adaptive and computational models of natural evolutionary systems - genetic algorithms, evolution strategies, evolutionary programming and genetic programming. In addition, they work well in the search for global solutions to optimization problems, allowing the production of optimization software that is robust and easy to implement. Furthermore, these algorithms can easily be hybridized with traditional optimization techniques. This book presents state-of-the-art lectures delivered by international academic and industrial experts in the field of evolutionary computing. It bridges artificial intelligence and scientific computing with a particular emphasis on real-life problems encountered in application-oriented sectors, such as aerospace, electronics, telecommunications, energy and economics. This rapidly growing field, with its deep understanding and assesssment of complex problems in current practice, provides an effective, modern engineering tool. This book will therefore be of significant interest and value to all postgraduates, research scientists and practitioners facing complex optimization problems.

Preface

xiii

Acknowledgements

Part I Methodological aspects

(230)

Using Genetic Algorithms for Optimization: Technology Transfer in Action

(20)

J. Haataja

Why and how to optimize?

(1)

From problems to solutions

(1)

Evolution and optimization

(1)

A simple GA

(2)

Other methods for global optimization

(1)

Gradient-based local optimization methods

(2)

A comparison of methods

(6)

Parallel computing and GAs

(1)

Further information

(6)

References

(4)

An Introduction to Evolutionary Computation and Some Applications

(20)

D. B. Fogel

Introduction

(1)

Advantages of evolutionary computation

(7)

Current developments and applications

(7)

Conclusions

(5)

References

(4)

Evolutionary Computation: Recent Developments and Open Issues

(12)

K. De Jong

Introduction

(1)

The historical roots of EC

(1)

Basic EA components

(4)

New and important directions for EC research

(2)

Summary and conclusions

(5)

References

(4)

Some Recent Important Foundational Results in Evolutionary Computation

(18)

D. B. Fogel

Introduction

(1)

No free lunch theorem

(2)

Computational equivalence of representations

(5)

Schema theorem in the presence of random variation

(6)

Two-armed bandits and the optimal allocation of trials

(1)

Conclusions

(4)

References

(3)

Evolutionary Algorithms for Engineering Applications

(22)

Z. Michalewicz

K. Deb

M. Schmidt

Th. Stidsen

Introduction

(2)

Constraint-handling methods

(4)

A need for a test case generator

(2)

The test case generator

(4)

An example

(5)

Summary

(5)

References

(4)

Embedded Path Tracing and Neighbourhood Search Techniques

(18)

C. R. Reeves

T. Yamada

Introduction

(2)

Avoiding local optima

(1)

Path tracing

(1)

Links to genetic algorithms

(1)

Embedded path tracing in scheduling problems

(2)

The genetic algorithm framework

101

(3)

Results

104

(5)

Conclusions

109

(4)

References

110

(3)

Parallel and Distributed Evolutionary Algorithms

113

(22)

M. Tomassini

Introduction

113

(1)

Genetic algorithms and genetic programming

114

(4)

Parallel and distributed evolutionary algorithms

118

(12)

Summary and conclusions

130

(5)

References

131

(4)

Evolutionary Multi-Criterion Optimization

135

(28)

K. Deb

Introduction

135

(1)

Principles of multi-criterion optimization

136

(2)

Classical methods

138

(3)

Evolutionary methods

141

(5)

Proof-of-principle results

146

(4)

An engineering design

150

(4)

Future directions for research

154

(4)

Summary

158

(5)

References

158

(5)

ACO Algorithms for the Traveling Salesman Problem

163

(22)

Th. Stutzle

M. Dorigo

Introduction

163

(1)

The traveling salesman problem

164

(1)

Available ACO algorithms for the TSP

165

(7)

Local search for the TSP

172

(1)

Experimental results

173

(4)

Other applications of ACO algorithms

177

(8)

References

179

(6)

Genetic Programming: Turing's Third Way to Achieve Machine Intelligence

185

(14)

J. R. Koza

F. H Bennett III

D. Andre

M. A. Keane

References

195

(4)

Automatic Synthesis of the Topology and Sizing for Analog Electrical Circuits Using Genetic Programming

199

(32)

F. H. Bennett III

M. A. Keane

D. Andre

J. R. Koza

Introduction

199

(1)

Seven problems of analog design

200

(1)

Background on genetic programming

201

(2)

Applying genetic programming to analog circuit synthesis

203

(6)

Preparatory steps

209

(6)

Results

215

(10)

Other circuits

225

(1)

Conclusion

226

(5)

References

226

(5)

Part II APPLICATION-ORIENTED APPROACHES

231

(150)

Multidisciplinary Hybrid Constrained GA Optimization

233

(28)

G.S. Dulikravich

T.J. Martin

B.H. Dennis

N.F. Foster

Introduction

233

(1)

Hybrid constrained optimization

234

(5)

Automated switching among the optimizers

239

(3)

Aero-thermal optimization of a gas turbine blade

242

(5)

Hypersonic shape optimization

247

(4)

Aerodynamic shape optimization of turbomachinery cascades

251

(5)

Maximizing multistage axial gas turbine efficiency

256

(1)

Summary

257

(4)

References

258

(3)

Genetic Algorithm as a Tool for Solving Electrical Engineering Problems

261

(22)

M. Rudnicki

P.S. Szczepaniak

P. Cholajda

Evolutionary algorithms

261

(5)

Genetic interpretation of DGA results in power transformers

266

(7)

Neural networks and stochastic optimisation for induction motor parameter identification

273

(6)

Conclusion

279

(1)

Summary

279

(4)

References

279

(4)

Genetic Algorithms in Shape Optimization: Finite and Boundary Element Applications

283

(44)

M. Cerrolaza

W. Annicchiarico

Introduction

283

(2)

A short review on genetic algorithms

285

(5)

Boundary modelling using b-splines curves

290

(3)

A GAs software for FE models optimization

293

(8)

Some engineering numerical examples

301

(22)

Concluding remarks

323

(4)

References

323

(4)

Genetic Algorithms and Fractals

327

(24)

E. Lutton

Introduction

327

(2)

Theoretical analysis

329

(9)

Resolution of a ``fractal'' inverse problem

338

(7)

Concluding remarks and future directions

345

(6)

References

346

(5)

Three Evolutionary Approaches to Clustering

351

(30)

H. Luchian

Introduction

351

(1)

Clustering problem no. 1: automated unsupervised clustering - the quantitative case

352

(12)

Clustering problem no. 2: a mobile-phone problem

364

(3)

Clustering problem no. 3: cross-clustering

367

(5)

(Clustering) Problem no. 4: electric power distribution

372

(2)

Conclusions

374

(7)

References

375

(6)

Part III INDUSTRIAL APPLICATIONS

381

(100)

Evolutionary Algorithms Applied to Academic and Industrial Test Cases

383

(16)

T. Back

W. Haase

B. Naujoks

L. Onesti

A. Turchet

Introduction to evolutionary strategies

383

(2)

Mathematical test cases

385

(5)

Multi-point airfoil design

390

(5)

Conclusions and outlook

395

(4)

References

396

(3)

Optimization of an Active Noise Control System inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm

399

(12)

Z. G. Diamantis

D. T. Tsahalis

I. Borchers

Introduction

400

(1)

Description of the test case article and the optimization problem

400

(2)

Genetic algorithms

402

(6)

Results and conclusions

408

(3)

References

409

(2)

Generator Scheduling in Power Systems by Genetic Algorithm and Expert System

411

(14)

B. Galvan

G. Winter

D. Greiner

M. Cruz

S. Cabrera

Introduction

411

(1)

Test system. Fitness function and coding schemes

412

(2)

The coding scheme

414

(2)

Method proposed

416

(1)

Final results

417

(2)

Best solution

419

(1)

Conclusions

420

(5)

Appendix

420

(1)

References

421

(4)

Efficient Partitioning Methods for 3-D Unstructured Grids Using Genetic Algorithms

425

(10)

A.P. Giotis

K.C. Giannakoglou

B. Mantel

J. Periaux

Introduction

425

(1)

Basic partitioning tools

426

(3)

Acceleration techniques

429

(1)

The proposed UGP algorithms

430

(1)

Applications - assessment in 3-D problems

431

(2)

Conclusions

433

(2)

References

434

(1)

Genetic Algorithms in Shape Optimization of a Paper Machine Headbox

435

(10)

J.P. Hamalainen

T. Malkamaki

J. Toivanen

Introduction

435

(1)

Flow problem in a header

436

(1)

Shape optimization of a header

437

(1)

Genetic algorithms applied to shape optimization

437

(2)

Numerical examples

439

(3)

Conclusions

442

(3)

References

442

(3)

A Parallel Genetic Algorithm for Multi-Objective Optimization in Computational Fluid Dynamics

445

(12)

N. Marco

S. Lanteri

J.-A. Desideri

J. Periaux

Introduction

445

(1)

Multi-objective optimization problem

446

(4)

Three-objective optimization problem

450

(1)

Multi-objective optimization in shape optimum design

451

(3)

Concluding remarks

454

(3)

References

455

(2)

Application of a Multi Objective Genetic Algorithm and a Neural Network to the Optimisation of Foundry Processes

457

(14)

G. Meneghetti

V. Pediroda

C. Poloni

Introduction

457

(1)

The simulation of foundary processes and foundamental equations

458

(1)

Optimisation tool

459

(3)

Object of the study and adopted optimisation procedure

462

(2)

Results and discussion

464

(3)

Conclusions

467

(4)

References

468

(3)

Circuit Partitioning Using Evolution Algorithms

471

(10)

J.A. Montiel-Nelson

G. Winter

L.M. Martinez

D. Greiner

Introduction

471

(1)

Related work

472

(1)

Problem formulation and cost function definition

472

(2)

Simulated annealing algorithm for circuit partitioning

474

(1)

Partitioning codification for genetic algorithm solution

475

(1)

Benchmarks

476

(3)

Experiments

479

(1)

Conclusions

479

(2)

References

481

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Evolutionary Algorithms in Engineering and Computer Science Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications

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