Evolutionary Algorithms in Engineering Applications / Edition 1by Dipankar Dasgupta
Pub. Date: 12/07/2010
Publisher: Springer Berlin Heidelberg
Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers,… See more details below
Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.
- Springer Berlin Heidelberg
- Publication date:
- Edition description:
- Softcover reprint of hardcover 1st ed. 1997
- Product dimensions:
- 1.17(w) x 9.21(h) x 6.14(d)
Table of Contents
I Introduction.- Evolutionary Algorithms — An Overview.- Robust Encodings in Genetic Algorithms.- II Architecture and Civil Engineering.- Genetic Engineering and Design Problems.- The Generation of Form Using an Evolutionary Approach.- Evolutionary Optimization of Composite Structures.- Flaw Detection and Configuration with Genetic Algorithms.- A Genetic Algorithm Approach for River Management.- Hazards in Genetic Design Methodologies.- III Computer Science and Engineering.- The Identification and Characterization of Workload Classes.- Lossless and Lossy Data Compression.- Database Design with Genetic Algorithms.- Designing Multiprocessor Scheduling Algorithms Using a Distributed Genetic Algorithm System.- Prototype Based Supervised Concept Learning Using Genetic Algorithms.- Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms.- Gate-Level Evolvable Hardware: Empirical Study and Application.- Physical Design of VLSI Circuits and the Application of Genetic Algorithms.- Statistical Generalization of Performance-Related Heuristics for Knowledge-Lean Applications.- IV Electrical, Control and Signal Processing.- Optimal Scheduling of Thermal Power Generation Using Evolutionary Algorithms.- Genetic Algorithms and Genetic Programming for Control.- Global Structure Evolution and Local Parameter Learning for Control System Model Reductions.- Adaptive Recursive Filtering Using Evolutionary Algorithms.- Numerical Techniques for Efficient Sonar Bearing and Range Searching in the Near Field Using Genetic Algorithms.- Signal Design for Radar Imaging in Radar Astronomy: Genetic Optimization.- Evolutionary Algorithms in Target Acquisition and Sensor Fusion.- V Mechanical and Industrial Engineering.- Strategies for the Integration of Evolutionary/Adaptive Search with the Engineering Design Process.- Identification of Mechanical Inclusions.- GeneAS: A Robust Optimal Design Technique for Mechanical Component Design.- Genetic Algorithms for Optimal Cutting.- Practical Issues and Recent Advances in Job- and Open-Shop Scheduling.- The Key Steps to Achieve Mass Customization.
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