| 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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