Optimization techniques for solving complex problems:
"Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One: Covers methodologies for comple...
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2009
|
| Schriftenreihe: | Wiley series on parallel and distributed computing
|
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Zusammenfassung: | "Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One: Covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two: Delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms. Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings." -- Publisher's description. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXI, 476 S. Ill., graph. Darst. |
| ISBN: | 9780470293324 |
Internformat
MARC
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| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2009 | |
| 300 | |a XXI, 476 S. |b Ill., graph. Darst. | ||
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| 650 | 4 | |a Informatik | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Computer science |x Mathematics | |
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Datensatz im Suchindex
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| adam_text | Real-world problems and
modern optimization techniques
to solve them
ere, a team of international experts brings together
core ideas for solving complex problems in optimi¬
zation across a wide variety of real-world settings, including
computer science, engineering, transportation, telecommuni¬
cations, and bioinformatics.
Part One
—
covers methodologies for complex problem solving
including genetic programming, neural networks, genetic algo¬
rithms, hybrid evolutionary algorithms, and more.
Part Two—delves into applications including
DNA seguencing
and reconstruction, location of antennae in telecommunication
networks, metaheuristics, FPGAs, problems arising in telecom¬
munication networks, ¡mage processing, time series prediction,
and more.
All chapters contain examples that illustrate the applications
themselves as well as the actual performance of the algo¬
rithms. Optimization Techniques for Solving Complex Problems
is a valuable resource for practitioners and researchers who
work with optimization in real-world settings.
ENRIQUE ALBA is a Professor of Data Communications and
Evolutionary Algorithms at the University of Malaga, Spain.
CHRISTIAN BLUM is a Research Fellow at the ALBCOM research
group of the
Universität
Politècnica de
Catalunya,
Spain.
PEDRO
I
SASI
is a Professor of Artificial Intelligence at the University
Carlos III of Madrid, Spain. C0R0MOT0
LEÓN
is a Professor
of Language Processors and Distributed Programming at the
University of
La Laguna,
Spain. JUAN ANTONIO
GÓMEZ
is
a Professor of Computer Architecture and
Reconfigurable
Computing at the University of
Extremadura,
Spain.
CONTENTS
CONTRIBUTORS
xv
FOREWORD
xix
PREFACE
xxi
PART I METHODOLOGIES FOR COMPLEX PROBLEM
SOLVING
1
1
Generating Automatic Projections by Means of Genetic
Programming
3
C.
Estébanez
and R.
kler
1.1
Introduction
3
1.2
Background
4
1.3
Domains
6
1.4
Algorithmic Proposal
6
1.5
Experimental Analysis
9
1.6
Conclusions
11
References
13
2
Neural Lazy Local Learning
15
J. M. Vails, I. M.
Galván,
and P. Isasi
2.1
Introduction
15
2.2
Lazy Radial Basis Neural Networks
17
2.3
Experimental Analysis
22
2.4
Conclusions
28
References
30
3
Optimization Using Genetic Algorithms with Micropopulations
31
Y. Soez
3.1
Introduction
31
3.2
Algorithmic Proposal
33
3.3
Experimental Analysis: The Rastrigin Function
40
3.4
Conclusions
44
References
45
vii
VIU
CONTENTS
4
Analyzing Parallel Cellular Genetic Algorithms
49
G. Luque, E. Alba, and B. Dorronsoro
4.1
Introduction
49
4.2
Cellular Genetic Algorithms
50
4.3
Parallel Models for cGAs
51
4.4
Brief Survey of Parallel cGAs
52
4.5
Experimental Analysis
55
4.6
Conclusions
59
References
59
5
Evaluating New Advanced Multiobjective Metaheuristics
63
A. J.
Nebro,
J. J.
Durillo,
F.
Luna, and E. Alba
5.1
Introduction
63
5.2
Background
65
5.3
Description of the Metaheuristics
67
5.4
Experimental Methodology
69
5.5
Experimental Analysis
72
5.6
Conclusions
79
References
80
6
Canonical Metaheuristics for Dynamic Optimization Problems
83
G. Leguizamón, G.
Ordóñez,
S.
Molina, and
E. Alba
83
84
88
92
93
95
96
7
Solving Constrained Optimization Problems with Hybrid
Evolutionary Algorithms
101
С
Cotta
and A. J. Fernandez
7.1
Introduction
101
7.2
Strategies for Solving CCOPs with HEAs
103
7.3
Study Cases
105
7.4
Conclusions
114
References
115
8
Optimization of Time Series Using Parallel, Adaptive,
and Neural Techniques
123
J.
A. Gómez,
M. D.
Jaraíz,
Μ. Α.
Vega,
and J.
M.
Sanchez
8.1
Introduction
123
8.2
Time Series Identification
124
6.1
Introduction
6.2
Dynamic Optimization Problems
6.3
Canonical MHs for DOPs
6.4
Benchmarks
6.5
Metrics
6.6
Conclusions
References
CONTENTS
ІХ
8.3
Optimization
Problem 125
8.4
Algorithmic Proposal
130
8.5
Experimental Analysis
132
8.6
Conclusions
136
References
136
9
Using
Reconfigurable
Computing for the Optimization
of Cryptographic Algorithms
139
J. M.
Granado,
Μ. Α.
Vega,
J. M.
Sánchez,
and J. A.
Gómez
9.1
Introduction
139
9.2
Description of the Cryptographic Algorithms
140
9.3
Implementation Proposal
144
9.4
Expermental Analysis
153
9.5
Conclusions
154
References
155
10
Genetic Algorithms, Parallelism, and
Reconfigurable
Hardware
159
J. M.
Sánchez,
M.
Rubio,
M. A.
Vega, and
J. A.
Gómez
10.1
Introduction
159
10.2
State
of the Art
161
10.3
FPGA Problem Description and Solution
162
10.4
Algorithmic Proposal
169
10.5
Experimental Analysis
172
10.6
Conclusions
177
References
177
11
Divide and Conquer: Advanced Techniques
179
C.
León,
G.
Miranda, and C. Rodriguez
11.1
Introduction
179
11.2
Algorithm of the Skeleton
180
11.3
Experimental Analysis
185
11.4
Conclusions
189
References
190
12
Tools for Tree Searches: Branch-and-Bound and A* Algorithms
193
С
León,
G.
Miranda, and
С
Rodríguez
12.1
Introduction
193
12.2
Background
195
12.3
Algorithmic Skeleton for Tree Searches
196
12.4
Experimentation Methodology
199
12.5
Experimental Results
202
12.6
Conclusions
205
References
206
X
CONTENTS
13
Tools for Tree Searches: Dynamic Programming
209
С
León,
G.
Miranda, and
С
Rodríguez
13.1
Introduction
209
13.2
Top-Down Approach
210
13.3
Bottom-Up Approach
212
13.4
Automata Theory and Dynamic Programming
215
13.5
Parallel Algorithms
223
13.6
Dynamic Programming Heuristics
225
13.7
Conclusions
228
References
229
PARTII
APPLICATIONS
231
14
Automatic Search of Behavior Strategies in Auctions
233
D.
Quintana
and
A. Mochan
14.1
Introduction
233
14.2
Evolutionary Techniques in Auctions
234
14.3
Theoretical Framework: The Ausubel Auction
238
14.4
Algorithmic Proposal
241
14.5
Experimental Analysis
243
14.6
Conclusions
246
References
247
15
Evolving Rules for Local Time Series Prediction
249
C. Luque, J. M. Vails, and P. Isasi
15.1
Introduction
249
15.2
Evolutionary Algorithms for Generating Prediction Rules
250
15.3
Experimental Methodology
250
15.4
Experiments
256
15.5
Conclusions
262
References
263
16
Metaheuristics in Bioinformatics:
DNA
Sequencing
and Reconstruction
265
C.
Cotta,
A. J.
Fernandez,
J. E.
Gallardo,
G.
Luque, and
E. Alba
16.1
Introduction
265
16.2
Metaheuristics and Bioinformatics
266
16.3 DNA Fragment
Assembly Problem
270
16.4
Shortest Common Supersequence Problem
278
16.5
Conclusions
282
References
283
CONTENTS
ХІ
17 Optimal
Location
of Antennas in Telecommunication Networks
287
G. Molina, F.
Chicano,
and
E. Alba
17.1
Introduction
287
17.2
State of the Art
288
17.3
Radio Network Design Problem
292
17.4
Optimization Algorithms
294
17.5
Basic Problems
297
17.6
Advanced Problem
303
17.7
Conclusions
305
References
306
18
Optimization of Image-Processing Algorithms Using FPGAs
309
M.
A. Vega, A. Gómez,
J-
A. Gómez, and
J. M.
Sanchez
18.1
Introduction
309
18.2
Background
310
18.3
Main Features of FPGA-Based Image Processing
311
18.4
Advanced Details
312
18.5
Experimental Analysis: Software Versus FPGA
321
18.6
Conclusions
322
References
323
19
Application of Cellular Automata Algorithms to the Parallel
Simulation of Laser Dynamics
325
J. L.
Guisado,
F.
Jiménez-Morales,
J. M.
Guerra, and
F.
Fernandez
19.1
Introduction
325
19.2
Background
326
19.3
Laser Dynamics Problem
328
19.4
Algorithmic Proposal
329
19.5
Experimental Analysis
331
19.6
Parallel Implementation of the Algorithm
336
19.7
Conclusions
344
References
344
20
Dense Stereo Disparity from an Artificial Life Standpoint
347
G. Olague, F.
Fernández,
С. В.
Pérez, and
E.
Lutton
20.1
Introduction
347
20.2
Infection Algorithm with an Evolutionary Approach
351
20.3
Experimental Analysis
360
20.4
Conclusions
363
References
363
21
Exact, Metaheuristic, and Hybrid Approaches
to Multidimensional Knapsack Problems
365
J. E.
Gallardo,
С.
Cotta,
and A. J. Fernandez
21.1
Introduction
365
XÜ
CONTENTS
21.2
Multidimensional Knapsack Problem
370
21.3
Hybrid Models
372
21.4
Experimental Analysis
377
21.5
Conclusions
379
References
380
22
Greedy Seeding and Problem-Specific Operators for GAs
Solution of Strip Packing Problems
385
C. Salto, J. M.
Molina, and
E. Alba
22.1
Introduction
385
22.2
Background
386
22.3
Hybrid GA for the 2SPP
387
22.4
Genetic Operators for Solving the 2SPP
388
22.5
Initial Seeding
390
22.6
Implementation of the Algorithms
391
22.7
Experimental Analysis
392
22.8
Conclusions
403
References
404
23
Solving the KCT Problem: Large-Scale Neighborhood Search
and Solution Merging
407
С
Blum and
M. J.
Blesa
23.1
Introduction
407
23.2
Hybrid Algorithms for the KCT Problem
409
23.3
Experimental Analysis
415
23.4
Conclusions
416
References
419
24
Experimental Study of
G
А
-Based Schedulers in Dynamic
Distributed Computing Environments
423
F. Xhafa and J.
Carretem
24.1
Introduction
423
24.2
Related Work
425
24.3
Independent Job Scheduling Problem
426
24.4
Genetic Algorithms for Scheduling in Grid Systems
428
24.5
Grid Simulator
429
24.6
Interface for Using a GA-Based Scheduler
with the Grid Simulator
432
24.7
Experimental Analysis
433
24.8
Conclusions
438
References
439
25
Remote Optimization Service
443
J.
García-Nieto,
F.
Chicano,
and
E. Alba
25.1
Introduction
443
CONTENTS
ХІІІ
25.2
Background
and State of the Art
444
25.3
ROS
Architecture
446
25.4
Information Exchange in
ROS
448
25.5
XML in
ROS
449
25.6
Wrappers
450
25.7
Evaluation of
ROS
451
25.8
Conclusions
454
References
455
26
Remote Services for Advanced Problem Optimization
457
J.
A. Gómez,
Μ. Α.
Vega,
J. M.
Sánchez,
J. L.
Guisado,
D.
Lombraña,
and F.
Fernández
26.1
Introduction
457
26.2
SIRVA
458
26.3
MOSET
and TIDESI
462
26.4
ABACUS
465
References
470
INDEX
473
|
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| discipline | Informatik Mathematik |
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| oclc_num | 230181154 |
| open_access_boolean | |
| owner | DE-703 DE-11 |
| owner_facet | DE-703 DE-11 |
| physical | XXI, 476 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series on parallel and distributed computing |
| spelling | Optimization techniques for solving complex problems ed. by Enrique Alba ... Hoboken, NJ Wiley 2009 XXI, 476 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series on parallel and distributed computing Includes bibliographical references and index "Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One: Covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two: Delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms. Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings." -- Publisher's description. Informatik Mathematik Computer science Mathematics Mathematical optimization Problem solving Optimierung (DE-588)4043664-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Optimierung (DE-588)4043664-0 s DE-604 Alba, Enrique Sonstige oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018627458&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018627458&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Optimization techniques for solving complex problems Informatik Mathematik Computer science Mathematics Mathematical optimization Problem solving Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4143413-4 |
| title | Optimization techniques for solving complex problems |
| title_auth | Optimization techniques for solving complex problems |
| title_exact_search | Optimization techniques for solving complex problems |
| title_full | Optimization techniques for solving complex problems ed. by Enrique Alba ... |
| title_fullStr | Optimization techniques for solving complex problems ed. by Enrique Alba ... |
| title_full_unstemmed | Optimization techniques for solving complex problems ed. by Enrique Alba ... |
| title_short | Optimization techniques for solving complex problems |
| title_sort | optimization techniques for solving complex problems |
| topic | Informatik Mathematik Computer science Mathematics Mathematical optimization Problem solving Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Informatik Mathematik Computer science Mathematics Mathematical optimization Problem solving Optimierung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018627458&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018627458&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT albaenrique optimizationtechniquesforsolvingcomplexproblems |