Information Processing with Evolutionary Algorithms

1. Front Matter

   Pages i-xviii

   PDF

2. Adaptive Business Intelligence Based on Evolution Strategies: Some Application Examples of Self-Adaptive Software

   • T. Bäck

   Pages 1-9

3. Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine Code Based, Linear Genetic Programming

   • L. M. Deschaine, F.D. Francone

   Pages 11-30

4. Evolutionary Optimization of Approximating Triangulations for Surface Reconstruction from Unstructured 3D Data

   • K. Weinert, J. Mehnen, M. Schneider

   Pages 31-44

5. An Evolutionary Algorithm Based on Morphological Associative Memories for Endmember Selection in Hyperspectral Images

   • M. Graña, C. Hernandez, A. d’Anjou

   Pages 45-59

6. On a Gradient-based Evolution Strategy for Parametric Illumination Correction

   • Elsa Fernández, Manuel Graña, Jesús Ruiz-Cabello

   Pages 61-72

7. A New Chromosome Codification for Scheduling Problems

   • Ramiro Varela, Camino R. Vela, Jorge Puente, David Serrano, Ana Suárez

   Pages 74-82

8. Evolution-based Learning of Ontological Knowledge for a Large-scale Multi-agent Simulation

   • A. Lazar, R.G. Reynolds

   Pages 83-97

9. An Evolutionary Algorithms Approach to Phylogenetic Tree Construction

   • C. B. Congdon

   Pages 99-116

10. Robot Controller Evolution with Macroevolutionary Algorithms

    • J. A. Becerra, J. Santos, R.J. Duro

    Pages 117-127

11. Evolving Natural Language Grammars

    • W. Cyre

    Pages 129-142

12. Evaluating Protein Structure Prediction Models with Evolutionary Algorithms

    • J. Gamalielsson, B. Olsson

    Pages 143-158

13. Learning Decision Rules by Means of Hybrid-Encoded Evolutionary Algorithms

    • J.C. Riquelme, J.S. Aguilar-Ruiz

    Pages 159-175

14. Evolvable Hardware Techniques for Gate-Level Synthesis of Combinational Circuits

    • A. Hernández-Aguirre

    Pages 177-194

15. The Evolutionary Learning Rule in System Identification

    • Oscar Montiel, Oscar Castillo, Patricia Melin, Roberto Sepulveda

    Pages 195-212

16. Current and Future Research Trends in Evolutionary Multiobjective Optimization

    • C. A. Coello Coello, G. Toscano Pulido, E. Mezura Montes

    Pages 213-231

17. Genetic Algorithms with Limited Convergence

    • J. Kubalík, L. Rothkrantz, J. Lažanský

    Pages 233-253

18. Evolution with Sampled Fitness Functions

    • F. Bellas, J. A. Becerra, R. J. Duro

    Pages 255-267

19. Molecular Computing by Signaling Pathways

    • J.-Q. Liu, K. Shimohara

    Pages 269-284

20. Strategy-Oriented Evolutionary Games: Toward a Grammatical Model of Games

    • B. Cases, S.O. Anchorena

    Pages 285-304

The last decade of the 20th century has witnessed a surge of interest in num- ical, computation-intensive approaches to information processing. The lines that draw the boundaries among statistics, optimization, arti cial intelligence and information processing are disappearing, and it is not uncommon to nd well-founded and sophisticated mathematical approaches in application - mains traditionally associated with ad-hoc programming. Heuristics has - come a branch of optimization and statistics. Clustering is applied to analyze soft data and to provide fast indexing in the World Wide Web. Non-trivial matrix algebra is at the heart of the last advances in computer vision. The breakthrough impulse was, apparently, due to the rise of the interest in arti cial neural networks, after its rediscovery in the late 1980s. Disguised as ANN, numerical and statistical methods made an appearance in the - formation processing scene, and others followed. A key component in many intelligent computational processing is the search for an optimal value of some function. Sometimes, this function is not evident and it must be made explicit in order to formulate the problem as an optimization problem. The search - ten takes place in high-dimensional spaces that can be either discrete, or c- tinuous or mixed. The shape of the high-dimensional surface that corresponds to the optimized function is usually very complex. Evolutionary algorithms are increasingly being applied to information processing applications that require any kind of optimization.

• 3D
• Computer Graphi
• Evolutionary Algorithms
• Hardware Design
• Image Processing
• Information Processing
• Process Control
• Robotics
• Triangulation
• algorithms
• artificial intelligence
• evolutionary algorithm
• genetic programming
• learning
• natural language
• algorithm analysis and problem complexity

• Dept. CCIA, Universidad Pais Vasco, Spain

  Manuel Graña, Alicia d’Anjou

• Grupo de Sistemas Autónomos, Universidade da Coruña, Spain

  Richard J. Duro

• Duke University, Durham, USA

  Paul P. Wang

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