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

• ISBN13: 9780471999027
• ISBN10: 0471999024
• Edition: 1st
• Format: Hardcover
• Copyright: 1999-07-09
• Publisher: WILEY

Summary

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.

Author Biography

K. Miettinen is the editor of Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, published by Wiley.

Pekka Neittaanmäki is the editor of Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, published by Wiley.

M. M. Mäkelä is the editor of Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, published by Wiley.

Jacques Périaux is the editor of Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, published by Wiley.

Table of Contents

Preface	xiii
Acknowledgements	xv
Part I Methodological aspects	1	(230)
Using Genetic Algorithms for Optimization: Technology Transfer in Action	3	(20)
J. Haataja
Why and how to optimize?	3	(1)
From problems to solutions	4	(1)
Evolution and optimization	5	(1)
A simple GA	6	(2)
Other methods for global optimization	8	(1)
Gradient-based local optimization methods	9	(2)
A comparison of methods	11	(6)
Parallel computing and GAs	17	(1)
Further information	17	(6)
References	19	(4)
An Introduction to Evolutionary Computation and Some Applications	23	(20)
D. B. Fogel
Introduction	23	(1)
Advantages of evolutionary computation	24	(7)
Current developments and applications	31	(7)
Conclusions	38	(5)
References	39	(4)
Evolutionary Computation: Recent Developments and Open Issues	43	(12)
K. De Jong
Introduction	43	(1)
The historical roots of EC	43	(1)
Basic EA components	44	(4)
New and important directions for EC research	48	(2)
Summary and conclusions	50	(5)
References	51	(4)
Some Recent Important Foundational Results in Evolutionary Computation	55	(18)
D. B. Fogel
Introduction	55	(1)
No free lunch theorem	55	(2)
Computational equivalence of representations	57	(5)
Schema theorem in the presence of random variation	62	(6)
Two-armed bandits and the optimal allocation of trials	68	(1)
Conclusions	69	(4)
References	70	(3)
Evolutionary Algorithms for Engineering Applications	73	(22)
Z. Michalewicz
K. Deb
M. Schmidt
Th. Stidsen
Introduction	73	(2)
Constraint-handling methods	75	(4)
A need for a test case generator	79	(2)
The test case generator	81	(4)
An example	85	(5)
Summary	90	(5)
References	91	(4)
Embedded Path Tracing and Neighbourhood Search Techniques	95	(18)
C. R. Reeves
T. Yamada
Introduction	95	(2)
Avoiding local optima	97	(1)
Path tracing	98	(1)
Links to genetic algorithms	98	(1)
Embedded path tracing in scheduling problems	99	(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