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Applied Evolutionary Algorithms in Java
Título:
Applied Evolutionary Algorithms in Java
ISBN:
9780387216157
Edición:
1st ed. 2003.
PRODUCTION_INFO:
New York, NY : Springer New York : Imprint: Springer, 2003.
Descripción física:
XIII, 219 p. online resource.
Contenido:
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 -- Appendix A -- A.1 Java-based EA Software -- A.2 C/C++ based EA Software -- A.3 General Evolution and Robotics References -- A.4 Java Reference Guides -- A.5 Useful References -- Appendix B -- A Genetic Algorithm Example and the GPSYS GP Library -- B.1 Basic Genetic Algorithm -- B.2 Simple Java Genetic Algorithm -- Exercises -- B.2.1 Vectors and Arraylists -- B.3 Application Design -- B.4 Eos: An Evolutionary and Ecosystem Research Platform -- Authors: Erwin Bonsma, Mark Shackleton and Rob Shipman -- B.4.1 Introduction -- B.4.2 Design Overview -- B.4.3 Key Classes -- B.4.4 Configuration -- B.4.5 Illustrative Example Systems -- B.4.6 Aerial Placement for Mobile Networks -- B.4.7 An Ecosystem Simulation Based on Echo -- B.4.8 Coevolutionary Function Optimisation -- B.4.9 Telecommunications Research using Eos -- B.4.10 NetGrow: A Telecommunications Application -- B.4.11 Eos Summary -- B.5 Traveling Salesman Problem -- B.5.1 EOS Traveling Salesman Problem -- B.6 Genetic Programming -- B.6.1 Observations from Running GPsys - Lawnmower Problem -- Eos References -- Appendix C -- C.1 Fuzzy Logic -- C.2 Fuzzy Set Theory -- C.2.1 Fuzzy Operators -- C.2.2 Linguistic Variables -- C.2.3 Fuzzy IF -- C.2.4 Fuzzy Associative Memories -- C.2.5 Fuzzy Control Systems -- C.2.6 Defuzzification -- C.2.7 Fuzzy Applications -- C.3 Limitations of Fuzzy Control -- C.3.1 Advantages of Fuzzy Systems -- C.4 Summary -- Further Reading -- Appendix D -- System Overview -- Use and License -- Programming Language and Run-Time Environment -- Top-Level Directory Files and Hierarchy -- Units of Measure -- The Client-Server Architecture -- Network/Local Connections Versus Dynamically Loaded Clients -- Why a Client-Server Architecture? -- Client-Server Communications -- Network and Local Connection Issues -- Communication via Events and Requests -- Keeping the RPI Protocol Language-Independent -- Configuration Elements and Properties Files -- The "port" and "hostName" Properties -- Overriding Properties -- Loading RsProperties Files as a Resource -- The Server -- Server Properties Files -- Accepting Clients -- The Scheduler -- The Floor Plan -- Syntax and Semantics -- Building a Virtual Robot -- Thinking About Client Design -- The Demonstration Clients -- Life Cycle of the Demonstration Clients -- How ClnMain Extends RsClient and Implements RsRunnable -- The RsRunnable Interface -- Building RsRunnable and RsClient into ClnMain -- DemoMain Implements RsRunnable, But Does Not Extend RsClient -- The Execution of ClnMain -- Uploading the Body Plan -- Registering Event Handlers -- Running the Event Loop -- How the Demo Clients Work -- Physical Layout of ClientZero -- The RsBody and RsBodyPart Classes -- RsBodyShape -- RsWheelSystem -- The Sensor Classes -- RsBodyTargetSensor -- RsBodyContactSensor -- Events and Requests.
Síntesis:
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.
Autor corporativo añadido:
Idioma:
Inglés