Evolutionary optimization /

The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly ad...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sarker, Ruhul A., Mohammadian, Masoud, Yao, Xin, 1962-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boston : Kluwer Academic Publishers, ©2002.
Colección:International series in operations research & management science ; 48.
Temas:
Mathematical optimization.
Operations research.
Evolutionary programming (Computer science)
Operations Research
Optimisation mathématique.
Recherche opérationnelle.
Programmation évolutive (Informatique)
Programmation évolutive.
MATHEMATICS > Game Theory.
Mathematical optimization
Operations research
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Contents
  • Preface
  • Contributing Authors
  • Part I Introduction
  • 1 Conventional Optimization Techniques
  • 1 Classifying Optimization Models
  • 2 Linear Programming
  • 3 Goal Programming
  • 4 Integer Programming
  • 5 Nonlinear Programming
  • 6 Simulation
  • 7 Further Reading
  • 2 Evolutionary Computation
  • 1 What Is Evolutionary Computation
  • 2 A Brief Overview of Evolutionary Computation
  • 3 Evolutionary Algorithm and Generate-and-Test Search Algorithm
  • 4 Search Operators
  • 5 Summary
  • Part II Single Objective Optimization
  • 3 Evolutionary Algorithms and Constrained Optimization
  • 1 Introduction
  • 2 General considerations
  • 3 Numerical optimization
  • 4 Final Remarks
  • 4 Constrained Evolutionary Optimization
  • 1 Introduction
  • 2 The Penalty Function Method
  • 3 Stochastic Ranking
  • 4 Global Competitive Ranking
  • 5 How Penalty Methods Work
  • 6 Experimental Study
  • 7 Conclusion
  • Appendix: Test Function Suite
  • Part III Multi-Objective Optimization
  • 5 Evolutionary Multiobjective Optimization
  • 1 Introduction
  • 2 Definitions
  • 3 Historical Roots
  • 4 A Quick Survey of EMOO Approaches
  • 5 Current Research
  • 6 Future Research Paths
  • 7 Summary
  • 6 MEA for Engineering Shape Design
  • 1 Introduction
  • 2 Multi-Objective Optimization and Pareto-Optimality
  • 3 Elitist Non-dominated Sorting GA (NSGA-II)
  • 4 Hybrid Approach
  • 5 Optimal Shape Design
  • 6 Simulation Results
  • 7 Conclusion
  • 7 Assessment Methodologies for MEAs
  • 1 Introduction
  • 2 Assessment Methodologies
  • 3 Discussion
  • 4 Comparing Two Algorithms: An Example
  • 5 Conclusions and Future Research Paths
  • Part IV Hybrid Algorithms
  • 8 Hybrid Genetic Algorithms
  • 1 Introduction
  • 2 Hybridizing GAs with Local Improvement Procedures
  • 3 Adaptive Memory GA's
  • 4 Summary
  • 9 Combining choices of heuristics
  • 1 Introduction
  • 2 GAs and parameterised algorithms
  • 3 Job Shop Scheduling
  • 4 Scheduling chicken catching
  • 5 Timetabling
  • 6 Discussion and future directions
  • 10 Nonlinear Constrained Optimization
  • 1 Introduction
  • 2 Previous Work
  • 3 A General Framework to look for SPdn
  • 4 Experimental Results
  • 5 Conclusions
  • Part V Parameter Selection in EAs
  • 11 Parameter Selection
  • 1 Introduction
  • 2 Parameter tuning vs. parameter control
  • 3 An example
  • 4 Classification of Control Techniques
  • 5 Various forms of control
  • 6 Discussion
  • Part VI Application of EAs to Practical Problems
  • 12 Design of Production Facilities
  • 1 Introduction
  • 2 Design for Material Flow When the Number of I/O Points is Unconstrained
  • 3 Design for Material Flow for a Single I/O Point
  • 4 Considering Intradepartmental Flow
  • 5 Material Handling System Design
  • 6 Concluding Remarks
  • 13 Virtual Population and Acceleration Techniques
  • 1 Introduction
  • 2 Concept of Virtual Population
  • 3 Solution Acceleration Techniques
  • 4 Accelerated GA and Acceleration Sche.

Ejemplares similares