Evolutionary Algorithms in Engineering Applications

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

Evolutionary algorithms are simple, easy to interface, and easy to extend. This volume discusses how they can be applied in different fields of engineering.