Applied Evolutionary Algorithms in Java Ghanea 1st edition by Hercock Robert 0387216157 9780387216157

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ISBN-10 :  0387216157 

ISBN-13 :  9780387216157

Author:  Hercock Robert 

Genetic algorithms provide a powerful range of methods for solving complex engineering search and optimization algorithms. Their power can also lead to difficulty for new researchers and students who wish to apply such evolution-based methods. Applied Evolutionary Algorithms in JAVA offers a practical, hands-on guide to applying such algorithms to engineering and scientific problems. The concepts are illustrated through clear examples, ranging from simple to more complex problems domains; all based on real-world industrial problems. Examples are taken from image processing, fuzzy-logic control systems, mobile robots, and telecommunication network optimization problems. The JAVA-based toolkit provides an easy-to-use and essential visual interface, with integrated graphing and analysis tools. Topics and features: inclusion of a complete JAVA toolkit for exploring evolutionary algorithms; strong use of visualization techniques, to increase understanding; coverage of all major evolutionary algorithms in common usage; broad range of industrially based example applications; includes examples and an appendix based on fuzzy logic.

Applied Evolutionary Algorithms in Java Ghanea 1st Table of contents:

1 Introduction to Evolutionary Computing
1.1 Evolutionary Computation
1.2 History of Evolutionary Computing
1.3 Obstacles to Evolutionary Computation
1.4 Machine Learning
1.5 Problem Domains
1.6 Applications
1.7 Evolution-Based Search
1.8 Summary
Further Reading
2 Principles of Natural Evolution
2.1 Natural Selection
2.2 DNA Structure
2.3 Summary
Further Reading
3 Genetic Algorithms
3.1 Genetic Algorithms
3.2 GA Basics
3.3 GA Theory
3.4 GA Operators
3.5 Pros and Cons of Genetic Algorithms
3.6 Selecting GA Methods
3.7 Example GA application
3.8 Summary
Further Reading
4 Genetic Programming
4.1 Genetic Programming
4.2 Introduction to Genetic Programming
4.3 GP Operators
4.4 Genetic Programming Implementation
4.5 Summary
Further Reading
5 Engineering Examples Using Genetic Algorithms
5.1 Introduction
5.2 Digital Image Processing
5.3 Basics of Image Processing
5.4 Java and Image Processing
5.5 Spectrographic Chromosome representation
5.6 Results
5.7 Summary — Evolved Image Processing
5.8 Mobile Robot Control
5.9 Behaviour Management
5.10 Evolutionary Methods
5.11 Fuzzy Logic Control
5.12 Evolved Fuzzy Systems
5.13 Robot Simulator
5.14 Analysis
5.15 Summary — Evolving Hybrid Systems
Further Reading
6 Future Directions in Evolutionary Computing
6.1 Developments in Evolutionary algorithms
6.2 Evolvable Hardware
6.3 Speciation and Distributed EA Methods
6.4 Advanced EA Techniques
6.5 Artificial Life and Coevolutionary Algorithms
6.6 Summary
Further Reading
7 The Future of Evolutionary Computing
7.1 Evolution in Action
7.2 Commercial Value of Evolutionary Algorithms
7.3 Future Directions in Evolutionary Computing
7.4 Conclusion

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Tags: Applied Evolutionary, Algorithms, Java Ghanea, Hercock Robert