Artists and creators in interactive art and interaction design have long been conducting research on human-machine interaction. Through artistic, conceptual, social and critical projects, they have shown how interactive digital processes are essential elements for their artistic creations. Resulting prototypes have often reached beyond the art arena into areas such as mobile computing, intelligent ambiences, intelligent architecture, fashionable technologies, ubiquitous computing and pervasive gaming. Many of the early artist-developed interactive technologies have influenced new design practices, products and services of today's media society. This book brings together key theoreticians and practitioners of this field. It shows how historically relevant the issues of interaction and interface design are, as they can be analyzed not only from an engineering point of view but from a social, artistic and conceptual, and even commercial angle as well.
Author(s): Uday K. Chakraborty
Series: Studies in Computational Intelligence 141
Edition: 1
Publisher: Springer
Year: 2008
Language: English
Pages: 342
front-matter......Page 1
Introduction......Page 10
Basic DE Research......Page 14
The Control Variables Np, F, and Cr......Page 15
Diversity Enhancement......Page 16
Problem Domain Specific Research......Page 24
Objective Functions with Single Objective......Page 25
Combinatorial Problems......Page 27
Design Centering......Page 30
Time-Variant Objective Functions......Page 32
An Example: Digital Filter Design......Page 33
More Topics and Outlook......Page 35
References......Page 36
Background......Page 41
Classic DE......Page 42
Decomposability and the Role of Cr......Page 44
The DE/ran/1/either-or Algorithm......Page 45
Drift Bias in DE’s Generating Function......Page 48
The Mutation Distribution M......Page 49
The Three-Vector Recombination Distribution R......Page 51
Naturally Distributed K......Page 55
Computing $E(S^{(\mu)})$......Page 58
Computing $E(S{^(X^3)})$......Page 60
Drift-Free Selection......Page 61
Drift-Free DE......Page 62
The Test Bed: Scalable Benchmark Functions......Page 65
Measuring Performance......Page 70
Discussion of Results......Page 71
Population Size......Page 91
Mutation Probability......Page 92
Default Control Parameter Settings......Page 93
Drift-Free DE’s Benefits......Page 94
References......Page 95
Introduction......Page 97
The Original DE Algorithm......Page 98
The Self-Adaptive Control Parameters in a jDE Algorithm......Page 99
The SA-DE Algorithm......Page 100
Experimental Results......Page 101
Discussion......Page 115
References......Page 116
Appendix......Page 118
Introduction......Page 119
Differential Evolution......Page 121
Improvement-Based Criteria......Page 124
Movement-Based Criteria......Page 125
Distribution-Based Criteria......Page 126
Combined Criteria......Page 127
Experimental Settings......Page 128
Criterion ImpAv......Page 131
Criterion NoAcc......Page 134
Criterion MaxDistQuick......Page 135
Criterion StdDevQuick......Page 138
Criterion Diff......Page 141
Conclusions......Page 142
References......Page 144
Introduction......Page 147
Constraint Violation and -Level Comparison......Page 149
The Properties of the Constrained Method......Page 150
Differential Evolution......Page 151
The Algorithm of the $\varepsilon$DE......Page 152
Controlling the $\varepsilon$-Level......Page 153
Test Problems and Experimental Conditions......Page 154
Experimental Results......Page 156
Comparison with the Stochastic Ranking Method......Page 159
References......Page 160
Introduction......Page 163
Opposition-Based Optimization......Page 165
Opposition-Based Population Initialization......Page 167
Opposition-Based Generation Jumping......Page 169
Comparison of DE and ODE......Page 171
Contribution of Opposite Points......Page 172
ODE with Variable Jumping Rate......Page 174
Investigated Jumping Rate Models......Page 175
Empirical Results......Page 176
Conclusion......Page 177
References......Page 178
Introduction......Page 180
Differential Evolution Variants......Page 181
Multi-objective Optimization......Page 185
Promoting Diversity......Page 186
Performing Elitism......Page 187
Non-Pareto-Based Approaches......Page 189
Pareto-Based Approaches......Page 190
Combined Approaches......Page 195
Convergence Properties of Multi-Objective Differential Evolution......Page 196
Conclusions and Future Research Paths......Page 197
References......Page 199
Introduction......Page 204
The Workings of DE......Page 205
Implementation of Parallel Evolutionary Algorithms......Page 207
Single Objective Optimization......Page 208
Multiobjective Optimization......Page 212
Computing Simultaneously Local and Global Minima......Page 217
Exploration vs. Exploitation......Page 218
The Unsupervised $k$--Windows Clustering Algorithm......Page 219
The Proposed Clustering Operator......Page 220
Experimental Results on Multi-minima Discovery......Page 221
Neural Network Training Using DE......Page 223
Training Integer Weight Neural Networks with Threshold Activations......Page 224
Experiments on Neural Network Training......Page 225
Data Clustering......Page 226
Designing an Efficient Clustering Fitness Criterion......Page 228
Evolutionary Clustering under the WDF Objective Function......Page 229
Evolutionary Clustering Results......Page 230
Real Life Application: DNA Microarrays......Page 234
Algorithms and Methodology......Page 235
Presentation of Experiments in Evolutionary Dimension Reduction......Page 236
Genetic Programming......Page 237
Genetically Programmed Differential Evolution Mutation Operators......Page 239
Experimental Discovery of Genetically Programmed Operators......Page 240
Synopsis......Page 242
References......Page 243
Introduction......Page 246
The DE Method in Antenna Applications......Page 247
The DE Method in Radiofrequency and Microwave Imaging......Page 253
Conclusions......Page 259
References......Page 260
Introduction......Page 263
Problem Constraints......Page 265
Economic Dispatch Using Differential Evolution......Page 266
Computer Simulation......Page 268
Dynamic Economic Dispatch......Page 269
System Constraints......Page 270
DED Using Hybrid Differential Evolution......Page 271
Simulation Results......Page 273
System Constraints......Page 275
UC Using Mixed Integer Hybrid Differential Evolution......Page 276
Simulation Results......Page 278
References......Page 279
Introduction......Page 280
Formulation of Economic Dispatch Problem......Page 281
Differential Evolution......Page 283
Chaotic Local Search......Page 285
Simulation Results......Page 287
Conclusion and Future Research......Page 289
References......Page 290
Introduction......Page 292
Related Work......Page 293
AODE Algorithm for Tuning a Chess Program......Page 294
Opposition......Page 295
Selection......Page 296
Adaptive Mutation......Page 297
Experiments......Page 298
Conclusions......Page 302
References......Page 303
Introduction......Page 304
Case Study I......Page 306
Case Study III......Page 308
Solution Representation and Evaluation......Page 309
Differential Evolution......Page 310
Real-Valued EA......Page 311
Fully Informed Particle Swarm......Page 312
Parameter Settings and Test Conditions......Page 313
Results......Page 314
Description......Page 316
Experimental Setup......Page 317
Results......Page 318
Conclusions and Further Work......Page 320
References......Page 321
Introduction......Page 323
RLV Model, Control Law and Clearance Criterion......Page 325
Clearance Criterion......Page 326
Optimisation Based Worst Case Analysis......Page 327
Random Initialisation......Page 328
Crossover......Page 329
Hybrid DE......Page 330
Worst-Case Analysis Results......Page 332
Conclusions......Page 335
References......Page 336
back-matter......Page 338
