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|a Evolutionary Algorithms for Embedded System Design
|h [electronic resource] /
|c edited by Rolf Drechsler, Nicole Drechsler.
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| 250 |
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|a 1st ed. 2003.
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| 264 |
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1 |
|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 2003.
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| 300 |
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|a XXVIII, 177 p.
|b online resource.
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|a Genetic Algorithms and Evolutionary Computation,
|x 1568-2587 ;
|v 10
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| 490 |
1 |
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|a Springer eBook Collection
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| 505 |
0 |
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|a 1 Evolutionary Testing of Embedded Systems -- 1. Introduction -- 2. Test Methods -- 3. Evolutionary Testing -- 4. Evolutionary Testing of Non-Functional Properties -- 5. Evolutionary Testing of Functional Behavior -- 6. Conclusion, Future Work -- 2 Genetic Algorithm Based DSP Code Optimization -- 1. Compilers for Digital Signal Processors -- 2. Address Generation in DSPs -- 3. Offset Assignment Problem -- 4. Genetic Algorithm Formulation -- 5. Experimental Results -- 6. Conclusions -- 3 Hierarchical Synthesis of Embedded Systems -- 1. Introduction -- 2. A Model for Embedded System Synthesis -- 3. System Synthesis -- 4. System Synthesis Using Evolutionary Algorithms -- 5. Hierarchical Design Space Exploration -- 6. Case Study -- 7. Conclusions -- 4 Functional Test Generation -- 1. Introduction -- 2. Functional Test Generation Algorithms -- 3. Proposed Hybrid Approach -- 4. Experimental Results -- 5. Concluding Remarks -- 5 Built-In Self Test of Sequential Circuits -- 1. Introduction -- 2. Cellular Automata -- 3. Test Architecture -- 4. The Selfish Gene Algorithm -- 5. Selfish Gene for CA-CSTP -- 6. Experimental Results -- 7. Conclusions.
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| 520 |
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|a Evolutionary Algorithms for Embedded System Design describes how Evolutionary Algorithm (EA) concepts can be applied to circuit and system design - an area where time-to-market demands are critical. EAs create an interesting alternative to other approaches since they can be scaled with the problem size and can be easily run on parallel computer systems. This book presents several successful EA techniques and shows how they can be applied at different levels of the design process. Starting on a high-level abstraction, where software components are dominant, several optimization steps are demonstrated, including DSP code optimization and test generation. Throughout the book, EAs are tested on real-world applications and on large problem instances. For each application the main criteria for the successful application in the corresponding domain are discussed. In addition, contributions from leading international researchers provide the reader with a variety of perspectives, including a special focus on the combination of EAs with problem specific heuristics. Evolutionary Algorithms for Embedded System Design is an excellent reference for both practitioners working in the area of circuit and system design and for researchers in the field of evolutionary concepts.
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| 590 |
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|a Loaded electronically.
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|a Electronic access restricted to members of the Holy Cross Community.
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| 650 |
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|a Electronic circuits.
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| 650 |
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|a Computer programming.
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| 650 |
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|a Computers.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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0 |
|a Computer-aided engineering.
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| 650 |
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0 |
|a Mathematical optimization.
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| 690 |
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|a Electronic resources (E-books)
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| 700 |
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|a Drechsler, Rolf.
|e editor.
|4 edt
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|a Drechsler, Nicole.
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|a Genetic Algorithms and Evolutionary Computation,
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|
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|a Springer eBook Collection.
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