Real-World Applications of Genetic Algorithms
eBook - PDF

Real-World Applications of Genetic Algorithms

  1. 378 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Real-World Applications of Genetic Algorithms

About this book

The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi-objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The book examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc.

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Yes, you can access Real-World Applications of Genetic Algorithms by Olympia Roeva in PDF and/or ePUB format, as well as other popular books in Computer Science & Neural Networks. We have over one million books available in our catalogue for you to explore.

Information

Publisher
IntechOpen
Year
2012
Print ISBN
9789535101468
eBook ISBN
9789535156895
Topic
Computer Science
Subtopic
Neural Networks
Index
Computer Science

Table of contents

  1. Real-World Applications of Genetic Algorithms
  2. Contents
  3. Preface
  4. Chapter 1 Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling
  5. Chapter 2 Application of Bio-Inspired Algorithms and Neural Networks for Optimal Design of Fractal Frequency Selective Surfaces
  6. Chapter 3 Evolutionary Multi-Objective Algorithms
  7. Chapter 4 Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems
  8. Chapter 5 Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes
  9. Chapter 6 A Hybrid Parallel Genetic Algorithm for Reliability Optimization
  10. Chapter 7 Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems
  11. Chapter 8 Hybrid Genetic Algorithm for Fast Electromagnetic Synthesis
  12. Chapter 9 A Hybrid Methodology Approach for Container Loading Problem Using Genetic Algorithm to Maximize the Weight Distribution of Cargo
  13. Chapter 10 Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times
  14. Chapter 11 Genetic Algorithms and Group Method of Data Handling- Type Neural Networks Applications in Poultry Science
  15. Chapter 12 New Approaches to Designing Genes by Evolution in the Computer
  16. Chapter 13 Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process
  17. Chapter 14 Multi-Objective Genetic Algorithm to Automatically Estimating the Input Parameters of Formant-Based Speech Synthesizers
  18. Chapter 15 Solving Timetable Problem by Genetic Algorithm and Heuristic Search Case Study: Universitas Pelita Harapan Timetable
  19. Chapter 16 Genetic Algorithms for Semi-Static Wavelength-Routed Optical Networks
  20. Chapter 17 Surrogate-Based Optimization