Genetic Algorithms and Evolutionary Computation

By: Rolf Drechsler (Editor), Nicole Drechsler (Editor)

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Hardcover | 30 November 2002

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212 Pages

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23.5 x 15.88 x 1.91

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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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Preface	p. ix
Contributing Authors	p. xiii
Foreword	p. xix
Introduction	p. xxi
Evolutionary Algorithms	p. xxii
Biological Background	p. xxii
Algorithmic Formulation	p. xxiii
Representation	p. xxiii
Objective Function	p. xxiv
Selection	p. xxiv
Initialization	p. xxv
Evolutionary Operators	p. xxv
Algorithm	p. xxvi
Contributions to this Book	p. xxvi
Evolutionary Testing of Embedded Systems	p. 1
Introduction	p. 1
Test Methods	p. 3
Comparative Evaluation	p. 5
Evolutionary Testing	p. 6
Suitability of Evolutionary Algorithms for the Evolutionary Test	p. 9
Tool Support	p. 12
Test Data Generation	p. 12
Test Driver	p. 13
Process Monitoring	p. 13
Configuration of Evolutionary Operators	p. 13
Evolutionary Testing of Non-Functional Properties	p. 14
Testing Temporal Behavior of Real-Time Systems	p. 15
Experiments on Sorting Methods	p. 16
Experiments on Computer-Graphics Example	p. 20
Safety and Robustness Testing	p. 23
Evolutionary Testing of Functional Behavior	p. 24
Experiments	p. 27
Conclusion, Future Work	p. 28
Genetic Algorithm Based DSP Code Optimization	p. 35
Compilers for Digital Signal Processors	p. 38
Address Generation in DSPs	p. 39
Offset Assignment Problem	p. 40
Motivating Example	p. 40
Simple Offset Assignment	p. 42
Generalized Offset Assignment	p. 43
Related Work	p. 47
Genetic Algorithm Formulation	p. 48
Motivation	p. 48
Chromosomal Representation	p. 49
Parameters and Initialization	p. 50
Crossover	p. 50
Mutation	p. 52
Fitness Function	p. 52
Experimental Results	p. 53
Statistical Evaluation	p. 53
Results for Application Programs	p. 56
Conclusions	p. 59
Hierarchical Synthesis of Embedded Systems	p. 63
Introduction	p. 64
Motivation	p. 64
Related Work	p. 64
Contribution	p. 65
A Model for Embedded System Synthesis	p. 66
The Specification Graph Model	p. 66
Hierarchical Modeling	p. 69
System Synthesis	p. 72
Implementation	p. 72
The Task of System Synthesis	p. 74
Objective Space	p. 79
System Synthesis Using Evolutionary Algorithms	p. 81
Multi-Objective Evolutionary Algorithms	p. 82
Strength Pareto Evolutionary Algorithm	p. 83
Chromosome Structure for System Synthesis	p. 85
The Function allocation()	p. 87
The Function binding()	p. 89
Hierarchical Design Space Exploration	p. 90
Pareto-Front Arithmetics	p. 91
Hierarchical Chromosomes	p. 93
Composite Mutation	p. 94
Composite Crossover	p. 96
Case Study	p. 97
Example	p. 97
Parameters of the Evolutionary Algorithm	p. 98
Exploration Results	p. 99
Pareto-Front Arithmetics	p. 100
Hierarchical Chromosomes	p. 101
Non-Hierarchical EAs	p. 101
Conclusions	p. 102
Functional Test Generation	p. 105
Introduction	p. 105
Functional Test Generation Algorithms	p. 110
Models	p. 110
Conjunctive Normal Form (CNF)	p. 111
Binary Decision Diagrams (BDDs)	p. 111
Assignment Decision Diagram (ADD)	p. 111
Finite State Machine	p. 113
Basic Concepts in Test Pattern Generation	p. 115
Justification, Implication and Backtracking	p. 115
SAT based Algorithms	p. 115
Time Frame Expansion	p. 115
Functional Test Generation Algorithms: A Taxonomy	p. 117
ATPGs based on Finite State Machines	p. 117
ATPGs based on Controllability, Observability and Structural Description of Data paths	p. 118
ATPG based on Assignment Decision Diagrams	p. 120
Genetic Approaches to Test Pattern Generation	p. 122
Gene Definition and Representation	p. 124
Fitness Function	p. 125
Crossover Operator	p. 125
Mutation Operator	p. 126
Selection Strategy	p. 126
Genetic Algorithms Advantages	p. 127
Genetic Algorithms Limitations	p. 127
Proposed Hybrid Approach	p. 127
Error Model	p. 128
GAs and BDDs Integration	p. 129
HTD and ETD Errors	p. 135
Experimental Results	p. 136
Concluding Remarks	p. 139
Built-In Self Test of Sequential Circuits	p. 143
Introduction	p. 143
Cellular Automata	p. 146
Test Architecture	p. 149
Circular Self-Test Path	p. 149
Cellular-Automata Circular Self-Test Path	p. 152
The Selfish Gene Algorithm	p. 154
Levels of Selection in Natural and Artificial Evolution	p. 155
Virtual Population	p. 158
High-Level Implementation	p. 159
Tournament	p. 161
Qualitative Analysis of Selfish Gene Behavior	p. 162
Polarization	p. 164
Selfish Gene for CA-CSTP	p. 164
Experimental Results	p. 166
Conclusions	p. 169
Index	p. 175
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Genetic Algorithms and Evolutionary Computation

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