Optimization Techniques for Solving Complex Problems
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More About This Title Optimization Techniques for Solving Complex Problems


Real-world problems and modern optimization techniques to solve them

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.


Enrique Alba is a Professor of Data Communications and Evolutionary Algorithms at the University of Málaga, Spain. Christian Blum is a Research Fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya, Spain.

Pedro Isasi?is a Professor of Artificial Intelligence at the University Carlos III of Madrid, Spain. Coromoto 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.



1. Generating Automatic Projections by Means of GP (C. Estébanez,and R. Aler).

1.1 Introduction.

1.2 Background.

1.3 Domains.

1.4 Algorithmic Proposal.

1.5 Experimental Analysis.

1.6 Conclusions and Future Work.


2. Neural Lazy Local Learning (J. M. Valls, I. M. Galván, and P. Isasi).

2.1 Introduction.

2.2 LRBNN: Lazy Radial Basis Neural Networks.

2.3 Experimental Framework.

2.4 Conclusions.


3. Optimization by Using GAs with Micropopulations (Y. Sáez).

3.1 Introduction.

3.2 Algorithmic Proposal.

3.3 Experimental Analysis: the Rastrigin Function.

3.4 Conclusions.


4. Analyzing Parallel Cellular Genetic Algorithms (G. Luque, E. Alba, and B. Dorronsoro).

4.1 Introduction.

4.2 Cellular Genetic Algorithms.

4.3 Parallel Models for cGAs.

4.4 Brief Survey on Parallel cGAs.

4.5 Experimental Results.

4.6 Conclusions.


5. Evaluating New Advanced Multiobjective Metaheuristics (A. J. Nebro, J.J. Durillo, F. Luna, and E. Alba).

5.1 Introduction.

5.2 Background.

5.3 Description of the Metaheuristics.

5.4 Experimentation Methodology.

5.5 Computational Results.

5.6 Conclusions and Future Work.


6. Canonical Metaheuristics for DOPs (G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba).

6.1 Introduction.

6.2 Dynamic Optimization Problems.

6.3 Canonical MHs for DOPs.

6.4 Benchmarks.

6.5 Metrics.

6.6 Conclusions.


7. Solving Constrained Optimization Problems with HEAs (C. Cotta, and A. J. Fernández).

7.1 Introduction.

7.2 Strategies for Solving CCOPs with HEAs.

7.3 Study Cases.

7.4 Conclusions.


8. Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques (J. A. Gomez, M. D. Jaraiz, M. A. Vega, and J. M. Sanchez).

8.1 Introduction.

8.2 Time Series Identification.

8.3 Optimization Problem.

8.4 Algorithmic Proposal.

8.5 Experimental Analysis.

8.6 Conclusions and Future Work.


9. Using Reconfigurable Computing to Optimization of Cryptographic Algorithms (J. M. Granado, M. A. Vega, J. M. Sanchez, and J. A. Gomez).

9.1 Introduction.

9.2 Description of the Cryptographic Algorithms.

9.3 Implementation Proposal.

9.4 Results.

9.5 Conclusions.


10. Genetic Algorithms, Parallelism and Reconfigurable Hardware (J. M. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez).

10.1 Introduction.

10.2 State of the Art.

10.3 FPGA Problem Description and Solution.

10.4 Algorithmic Proposal.

10.5 Experiments and Results.

10.6 Conclusions and Future Work.


11. Divide and Conquer, Advanced Techniques (C. Lóon, G. Miranda, and C. Rodriguez).

11.1 Introduction.

11.2 The Algorithm of the Skeleton.

11.3 Computational Results.

11.4 Conclusions.


12. Tools for Tree Searches: Branch and Bound and A* Algorithms (C. León, G. Miranda, and C. Rodriguez).

12.1 Introduction.

12.2 Background.

12.3 Algorithmic Skeleton for Tree Searches.

12.4 Experimentation Methodology.

12.5 Computational Results.

12.6 Conclusions and Future Work.


13. Tools for Tree Searches: Dynamic Programming (C. León, G. Miranda, and C. Rodriguez).

13.1 Introduction.

13.2 The TopDown.


13.3 The BottomUp Approach.

13.4 Automata Theory and Dynamic Programming.

13.5 Parallel Algorithms.

13.6 Dynamic Programming Heuristics.

13.7 Conclusions.



14. Automatic Search of Behavior Strategies in Auctions (D. Quintana, and A. Mochón).

14.1 Introduction.

14.2 Evolutionary Techniques in Auctions.

14.3 Theoretical Framework: the Ausubel Auction.

14.4 Algorithmic Proposal.

14.5 Experimental analysis.

14.6 Conclusions and Future Work.


15. Evolving Rules For Local Time Series Prediction (C. Luque, J. M. Valls, and P. Isasi).

15.1 Introduction.

15.2 Evolutionary Algorithms for Generating Prediction Rules.

15.3 Description of the Method.

15.4 Experiments.

15.5 Conclusions.


16. Metaheuristics in Bioinformatics (C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba).

16.1 Introduction.

16.2 Metaheuristics and Bioinformatics.

16.3 The DNA Fragment Assembly Problem.

16.4 The Shortest Common Supersequence Problem.

16.5 Conclusions.


17. Optimal Location of Antennae in Telecommunication Networks (G. Molina, F. Chicano, and E. Alba).

17.1 Introduction.

17.2 State of the Art.

17.3 Radio Network Design Problem.

17.4 Optimization Algorithms.

17.5 Basic Problem Instances.

17.6 Advanced Problem Instance.

17.7 Conclusions.


18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez).

18.1 Introduction.

18.2 Background.

18.3 Main Features of the FPGAbased Image Processing.

18.4 Advanced Details.

18.5 Experimental Analysis: Software vs. FPGA.

18.6 Conclusions.


19. Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics (J. L. Guisado, F. Jiménez Morales, J. M. Guerra, F. Fernández de Vega).

19.1 Introduction.

19.2 Background.

19.3 The Problem: Laser Dynamics.

19.4 Algorithmic Proposal.

19.5 Experimental Analysis.

19.6 Parallel Implementation of the Algorithm.

19.7 Conclusions and Future Work.


20. Dense Stereo Disparity from an ALife Standpoint (G. Olague, F. Fernandez, C. B. Perez, and E. Lutton).

20.1 Introduction.

20.2 Infection Algorithm with an Evolutionary Approach.

20.3 Experimental Results.

20.4 Conclusion.


21. Approaches to Multidimensional Knapsack Problems (J. E. Gallardo, C. Cotta, and A. J. Fernández).

21.1 Introduction.

21.2 The Multidimensional Knapsack Problem.

21.3 Hybrid Models.

21.4 Experimental Results.

21.5 Conclusions and Future Work.


22. Greedy Seeding and ProblemSpecific Operators for GAs Solving Strip Packing Problems (C. Salto, J. M. Molina, and E. Alba).

22.1 Introduction.

22.2 Background.

22.3 A Hybrid GA for the 2SPP.

22.4 Genetic Operators for Solving the 2SPP.

22.5 Initial Seeding.

22.6 Implementation.

22.7 Computational Analysis.

22.8 Conclusions.


23. Solving the KCT Problem: Large Scale Neighborhood Search and Solution Merging (C. Blum, and M. Blesa).

23.1 Introduction.

23.2 Hybrid Algorithms for the KCT Problem.

23.3 Experimental Evaluation.

23.4 Summary and Conclusions.


24. Experimental Study of Gabased Schedulers in Dynamic Distributed Computing Environments (F. Xhafa, and J. Carretero).

24.1 Introduction.

24.2 Related Work.

24.3 Independent Job Scheduling Problem.

24.4 Genetic Algorithms for Scheduling in Grid Systems.

24.5 Grid Simulator.

24.6 The Interface for Using Gabased Scheduler with the Grid Simulator.

24.7 Experimental Analysis.

24.8 Conclusions.


25. ROS: Remote Optimization Service (J. GarcíaNieto, F. Chicano, and E. Alba).

25.1 Introduction.

25.2 Background and State of the Art.

25.3 ROS Architecture.

25.4 Information Exchange in ROS.

25.5 XML in ROS.

25.6 Wrappers.

25.7 Evaluation of ROS.

25.8 Conclusions and Future Work.


26. SIRVA, MOSET, TIDESI, ABACUS: Remote Services for Advanced.

Problem Optimization (J. A. Gomez, M. A. Vega, J. M. Sanchez, J. L. Guisado, D. Lombrana, and F. Fernandez).

26.1 Introduction.

26.2 SIRVA.

26.3 MOSET and TIDESI.

26.4 ABACUS.