From the explosion of interest, research, and applications of evolutionary computation a new field emerges-evolutionary electronics. Focused on applying evolutionary computation concepts and techniques to the domain of electronics, many researchers now see it as holding the greatest potential for overcoming the drawbacks of conventional design techniques.Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms formally introduces and defines this area of research, presents its main challenges in electronic design, and explores emerging technologies. It describes the evolutionary computation paradigm and its primary algorithms, and explores topics of current interest, such as multi-objective optimization. The authors examine numerous evolutionary electronics applications, draw conclusions about those applications, and sketch the future of evolutionary computation and its applications in electronics. In coming years, the appearance of more and more advanced technologies will increase the complexity of optimization and synthesis problems, and evolutionary electronics will almost certainly become a key to solving those problems. Evolutionary Electronics is your key to discovering and unlocking the potential of this promising new field.
Author(s): Ricardo Salem Zebulum, Marco Aurelio Pacheco, Marley Maria Be Vellasco
Series: International Series on Computational Intelligence
Edition: 1
Publisher: CRC Press
Year: 2001
Language: English
Pages: 306
EVOLUTIONARY ELECTRONICS: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms......Page 1
PREFACE......Page 8
ACKNOWLEDGMENTS......Page 11
CONTENTS......Page 12
1.1 EVOLUTIONARY DESIGN OF ELECTRONIC CIRCUITS: A PROSPECT OF THE AREA......Page 17
1.2 ELECTRONIC CIRCUIT DESIGN: A SEARCH TASK......Page 22
1.3 A BRIEF SURVEY OF EVOLUTIONARY ELECTRONICS......Page 25
REFERENCES......Page 26
APPENDIX C: Gm-C FILTERS......Page 0
2.1.1 The Evolutionary Hypothesis......Page 31
2.1.2 Molecular Genetics......Page 33
2.1.3 Recombination and Mutation......Page 36
2.1.4 Mendelian Ratios......Page 37
2.1.5 The Evidences of Evolution......Page 39
2.2 PUTTING THE IDEAS TO WORK: EVOLUTIONARY COMPUTATION......Page 41
2.2.1 Representation......Page 43
2.2.3.1 Selection......Page 48
2.2.3.2 Crossover......Page 50
2.2.3.3 Mutation......Page 52
2.3.1 Evolutionary Programming (EP)......Page 53
2.3.2 Evolutionary Strategies (ES)......Page 54
2.3.3 Genetic Algorithms (GAs)......Page 55
2.3.4 Genetic Programming (GP)......Page 60
REFERENCES......Page 66
3.1 VARIABLE LENGTH REPRESENTATION SYSTEMS......Page 69
3.1.1 Representation in Natural Systems......Page 70
3.1.2 Representation in Artificial Systems......Page 71
3.1.3 Genetic Algorithms with Variable Length Representation......Page 72
3.2 EVOLUTIONARY ALGORITHMS USING THE MEMORY PARADIGM......Page 80
3.3 MULTIPLE-OBJECTIVE OPTIMIZATION......Page 81
3.3.1 Evolutionary Computation Applied to Multi-Objective Optimization......Page 82
3.3.1.2 Population-Based Non-Pareto Approaches......Page 83
3.3.1.3 Pareto-Based Approaches......Page 84
3.3.2 A New Approach for Multi-Objective Optimization: Energy Minimization Strategy......Page 85
3.4 SPECIATION IN EVOLUTIONARY ALGORITHMS......Page 89
3.5 DECEPTIONAND EPISTASY......Page 92
3.5.1 Epistasy......Page 95
REFERENCES......Page 103
4.1 OPTIMIZATION OF ANALOG VLSI CHIPS......Page 106
4.1.1 OpAmp Design Optimization......Page 107
4.1.2 Problem Representation......Page 108
4.1.4 Fitness Evaluation Function......Page 109
4.1.5.1 Miller CMOS OTA: GAs Rediscovering Human Design......Page 110
4.1.5.2 Low Power Operational Amplifiers: New Design Strategies......Page 113
4.1.5.3 Synthesis of BiCMOS Amplifiers......Page 119
4.2 DIGITAL VLSI DESIGN AND LAYOUT OPTIMIZATION......Page 121
4.2.1 Logic Synthesis......Page 122
Floorplanning......Page 124
Routing......Page 127
4.2.3 Testing......Page 128
4.3 SUMMARY......Page 131
REFERENCES:......Page 132
5.1 ANALOG CIRCUITS EVOLUTION......Page 134
5.1.1.1 Passive Filters: Basic Concepts......Page 135
5.1.1.2 Representation......Page 137
5.1.1.3 Fitness Evaluation......Page 139
5.1.1.4 Case Study: Low Pass Brick-Wall Filter......Page 140
5.1.1.5 Other Evolutionary Approaches......Page 143
5.1.1.5.1 Genetic Programming......Page 144
5.1.1.5.2 Genetic Algorithm with Developmental Representation......Page 151
5.1.2 Synthesis of Active Filters......Page 155
5.1.2.1 Problem Description......Page 156
5.1.2.3 Evaluation......Page 157
5.1.2.4.1 Single Objective Experiments......Page 158
5.1.2.4.2 Multi-Objective Experiments: First Case......Page 161
5.1.2.4.3 Multi-Objective Experiments: Second Case......Page 164
5.1.3.1 Problem Description......Page 167
5.1.3.3 Evaluation......Page 168
5.1.3.4 Case Studies......Page 169
5.1.4 Synthesis of a Digital to Analog Converter (DAC)......Page 174
REFERENCES......Page 177
CHAPTER 6: Evolutionary Computation: A Tool for Digital Circuit Synthesis......Page 180
6.1.1 Functional Level Representation......Page 181
6.1.2 Gate Level Representation......Page 186
6.1.3 Transistor Level Representation......Page 188
6.2 FITNESS EVALUATION FUNCTION......Page 189
6.3.1.1 Multiplexers and Parity Circuits......Page 192
6.3.1.2 Arithmetic Circuits......Page 195
6.3.1.2.1 Sum of Products Representation......Page 196
6.3.1.2.2 Use of Reverse Logic and Incremental Evolution......Page 197
6.3.1.2.3 Adders and Multipliers......Page 200
6.3.2 Synthesis of Sequential Circuits......Page 203
6.3.3 Transistor Logic......Page 205
6.3.4 Digital Filters......Page 209
6.3.4.2 Functional and Gate Level Representation......Page 210
6.3.4.3 Functional Level Representation in the Synthesis of Multiplier-less Filters......Page 211
6.3.4.3.1 Gate Level Representation......Page 214
REFERENCES......Page 218
CHAPTER 7: Evolution of Circuits on Reconfigurable Chips......Page 220
7.1 PROGRAM MABLE LOGIC DEVICES......Page 221
7.1.1 PROM......Page 222
7.1.2 Programmable Logic Array (PLA)......Page 223
7.1.3 Programmable Array Logic (PAL)......Page 225
7.1.4 Field Programmable Digital Arrays (FPGAs)......Page 226
7.2 FIELD PROGRAM MABLE ANALOG ARRAYS (FPAAs)......Page 229
7.2.2 Motorola......Page 230
7.2.3 Palmo (University of Edinburgh)......Page 232
7.2.4 Evolvable Motherboard (University of Sussex)......Page 234
7.2.5 Programmable Transistor Array (PTA) (Jet Propulsion Lab)......Page 235
7.2.6 Programmable Analog Multiplexer Array (PAMA) (Catholic University of Rio de Janeiro)......Page 237
7.2.8 Comparative Table......Page 239
7.3.1 Thompson......Page 240
7.3.1.1 Experiment Setup......Page 241
7.3.1.2 Results......Page 242
7.3.1.3 Analysis......Page 245
7.3.2 Stoica et al.......Page 247
7.3.3 Zebulum et al. (2000)......Page 249
7.3.4 Zebulum et al. (1999)......Page 253
7.4 VIRTUAL COMPUTING......Page 256
REFERENCES......Page 257
8.1 ACTIVE FILTERS......Page 260
8.2.1.2 Evaluation......Page 262
8.2.1.3 First Experiment......Page 264
8.2.1.5 Lessons Learned......Page 271
8.2.2.1 Representation......Page 272
8.2.2.2 Fitness Evaluation Function......Page 274
8.2.3 Comparison......Page 275
8.3.1 Overview of Antennas......Page 276
8.3.2.3 Fitness Evaluation Function......Page 278
8.3.3.2 Representation......Page 279
8.4 FAULTTOLERANCE AND EVOLUTIONARY ELECTRONICS......Page 280
8.4.1 Implicit Fault Tolerance......Page 281
8.4.2 Explicit Fault Tolerance......Page 283
8.4.3 A Comparison between Implicit and Explicit Fault Tolerance Techniques......Page 285
REFERENCES......Page 287
9.2 PROGRAMMABLE CIRCUITS......Page 289
9.3 CONSEQUENCES ON ANALOG AND DIGITAL ELECTRONICS......Page 291
9.4 FAULT TOLERANT SYSTEMS......Page 292
REFERENCES......Page 293
APPENDIX A: MOS TRANSISTORS......Page 294
REFERENCES......Page 296
APPENDIX B: SWITCHED CAPACITOR (SC) CIRCUITS......Page 297
REFERENCES......Page 298
APPENDIX C: Gm-C FILTERS......Page 299
REFERENCES......Page 300
The Single-Electron Transistor......Page 301
The Simulator of Nanostructures......Page 302
The Evolution of an Inverter Circuit......Page 303
Results......Page 304
REFERENCES......Page 305
