Author: Dipankar Dasgupta

Publisher: Springer Science & Business Media

Published: 1997-05-20

Total Pages: 584

ISBN-13: 9783540620211

Evolutionary algorithms - an overview. Robust encodings in genetic algorithms. 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. 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 heuristcs for knowledge-lean applications. 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. 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.