Genetic Programming: 10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007, ProceedingsMarc Ebner, Michael O'Neill, Anikó Ekárt, Leonardo Vanneschi, Anna Isabel Esparcia-Alcázar Springer, 2007. gada 20. jūn. - 382 lappuses This book constitutes the refereed proceedings of the 10th European Conference on Genetic Programming, EuroGP 2007, held in Valencia, Spain in April 2007 colocated with EvoCOP 2007. The 21 revised plenary papers and 14 revised poster papers were carefully reviewed and selected from 71 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas. |
No grāmatas satura
1.–5. rezultāts no 73.
2. lappuse
... shown to signficantly outperform a standard Genetic Algorithm on a scalable problem with regularities [4]. The genome of an MGA individual is a vector of genes, where each gene is comprised of two components, the number-of-repetitions ...
... shown to signficantly outperform a standard Genetic Algorithm on a scalable problem with regularities [4]. The genome of an MGA individual is a vector of genes, where each gene is comprised of two components, the number-of-repetitions ...
4. lappuse
... 1 To allow the creation of multiple building blocks of different sizes the following grammar could be adopted (again shown for 8-bit strings). <g> ::= "<bitstring> ::=" <reps>"<bbk4> ::=" <bbk4> "<bbk2> ::=" <bbk2>. 4 E. Hemberg et al.
... 1 To allow the creation of multiple building blocks of different sizes the following grammar could be adopted (again shown for 8-bit strings). <g> ::= "<bitstring> ::=" <reps>"<bbk4> ::=" <bbk4> "<bbk2> ::=" <bbk2>. 4 E. Hemberg et al.
14. lappuse
... shown to boost the AUC in [14], so it could suit our need, but it constrains the choice of the learner, that must either output values in {0,1} or in the [0,1] interval, or that requires a numerical search to optimize some internal ...
... shown to boost the AUC in [14], so it could suit our need, but it constrains the choice of the learner, that must either output values in {0,1} or in the [0,1] interval, or that requires a numerical search to optimize some internal ...
15. lappuse
... shown in Table2and serves as a fitness function for the evolutionary learner. It is very close to the one proposed by Sebag et al. in their ROGER experiments [10], except that the contribution of every learning case to the global area ...
... shown in Table2and serves as a fitness function for the evolutionary learner. It is very close to the one proposed by Sebag et al. in their ROGER experiments [10], except that the contribution of every learning case to the global area ...
18. lappuse
... shown in Table 5 while those for the ES are taken from [10]. For GP we used 3 operators for internal nodes (+,−,∗), ephemeral random constants (ERC) in the range [−2.0,+2.0], and as many inputs as needed by the problem at hand. Note ...
... shown in Table 5 while those for the ES are taken from [10]. For GP we used 3 operators for internal nodes (+,−,∗), ephemeral random constants (ERC) in the range [−2.0,+2.0], and as many inputs as needed by the problem at hand. Note ...
Saturs
Introduction | 193 |
Mathematical Preliminaries | 194 |
The Distribution of Tree Sizes Under Crossover | 196 |
Evolution of the TreeSize Distribution | 198 |
Subtree Distribution at the Crossover FixedPoint | 199 |
Conjecture or Theorem? | 200 |
Discussion and Conclusions | 203 |
Introduction | 205 |
23 | |
24 | |
26 | |
29 | |
31 | |
32 | |
33 | |
Sizes and Levels in Trees | 34 |
Empirical Behaviour of in Genetic Programming | 36 |
SizeLevel Covariance and Fitness | 40 |
Conclusion | 42 |
Introduction | 45 |
Inverse Problem and GP | 46 |
Density Estimation with GP | 47 |
Experiments | 50 |
Conclusion and Perspectives | 53 |
Introduction | 55 |
Related Work | 56 |
Fragment Schemas | 57 |
Maximal Fragments | 58 |
The TripS Algorithm | 59 |
Analysis of GP Using TripS | 60 |
Conclusions | 64 |
Algorithm to Find Histogram of Sizes of Contained Fragments | 66 |
Introduction | 68 |
Iterated Function Systems | 69 |
Representations of the Inverse Problem for IFS | 70 |
Results | 72 |
Conclusions | 76 |
Introduction | 78 |
Previous Work | 79 |
The MateInN Problem | 80 |
Evolving MateSolving Algorithms | 82 |
Results | 84 |
Concluding Remarks | 87 |
Introduction | 90 |
The Architecture of Graphics Processing Units | 91 |
Programming a GPU | 93 |
Parsing a GP Expression | 94 |
Benchmarks | 95 |
Conclusions | 99 |
Introduction | 102 |
The FIFTH Language | 103 |
The FIFTH Genetic Programming Environment | 105 |
Using GPE5 to Solve a Problem | 107 |
Example Problems | 108 |
Discussion | 111 |
References | 112 |
Introduction | 114 |
Background Program Induction with Guaranteed Behaviour | 115 |
Background Model Checking | 116 |
Model Checking as a Fitness Measure | 117 |
Methods Some Example Specifications | 118 |
Results | 122 |
Introduction | 125 |
Geometric PSO | 127 |
Geometric PSO for Specific Spaces | 131 |
Towards a Geometric PSO for GP and Other Representations | 134 |
Conclusions and Future Work | 135 |
Introduction | 137 |
Related Work | 138 |
Methodology | 139 |
Evaluation and Results | 142 |
Conclusion | 146 |
Introduction | 148 |
Layered Learning | 149 |
Layered Learning Using Testing Subsets | 151 |
Layered Learning Using Simplified Problems | 154 |
Conclusions | 157 |
References | 158 |
Introduction | 160 |
Ensemble for Streaming Data | 162 |
Adaptive GP Boosting Ensemble | 163 |
Experimental Results | 165 |
Conclusions | 168 |
Introduction | 170 |
RTT Estimation Problem | 171 |
Multiobjective Approaches for RTT Estimation | 174 |
Experimental Procedure | 175 |
Results | 177 |
Concluding Remarks | 179 |
Introduction | 181 |
Binary Decision Diagrams | 182 |
Algorithm | 183 |
The Promise | 185 |
Fitness Conservation and Generation Lag | 186 |
Evolvability Discrepancies | 187 |
Population Size and Neutrality | 188 |
Discussion | 189 |
Summary | 190 |
Cartesian Genetic Programming CGP | 207 |
Multichromosome Cartesian Genetic Programming | 208 |
Evolving a Prime Producing Formulae | 210 |
Results and Discussion | 212 |
Conclusion and Future Work | 215 |
Introduction | 217 |
VoIP | 218 |
VoIP Traffic Simulation | 219 |
Experimental Setup | 222 |
Results and Analysis | 224 |
Conclusions and Future Work | 226 |
Introduction | 229 |
The Paretocoevolutionary GP Classifier Algorithm | 231 |
Experiments | 234 |
Results | 236 |
Conclusion | 238 |
Introduction | 241 |
Definitions and Preliminary Results | 242 |
Neutrality Results | 245 |
Conclusions and Future Work | 248 |
Introduction | 251 |
Methods | 253 |
Results | 256 |
Discussion | 257 |
Conclusions | 258 |
Introduction | 261 |
Cartesian Genetic Programming CGP | 262 |
Embedded Cartesian Genetic Programming ECGP | 263 |
Applying CGP and ECGP to GAs | 264 |
Experiment Details | 266 |
Results | 267 |
Conclusion | 269 |
Introduction | 271 |
State of the Art and Related Work | 273 |
Code Regulation | 274 |
Experiments | 277 |
Conclusions and Outlook | 279 |
Introduction | 281 |
The Data | 282 |
Methodology | 283 |
Results | 284 |
Conclusions | 289 |
Introduction | 291 |
Object Oriented Versus Functional Program Spaces | 292 |
Evolvable Class Representation | 293 |
Results and Discussion | 298 |
Conclusions | 300 |
Introduction | 301 |
Programming Space Under Exploration | 302 |
Experimental Context | 305 |
Evaluating the Generality of the Experimental Setup | 306 |
Results and Discussion | 307 |
Conclusions | 309 |
Introduction | 311 |
Fitness Landscapes | 312 |
CGP at the Functional Level for Image Filter Evolution | 314 |
Proposed Genetic Operators | 315 |
Experimental Results | 316 |
Discussion | 317 |
Conclusions | 319 |
Introduction | 321 |
Parallel Machine Environment | 322 |
Scheduling with Genetic Programming | 324 |
Conclusion | 329 |
Introduction | 331 |
Food Particle Swarm FPS Model | 332 |
GP Approach | 333 |
Results | 334 |
Conclusion | 340 |
Introduction | 341 |
Approach | 343 |
Experiments | 345 |
Analysis | 348 |
Conclusions | 349 |
Introduction | 351 |
General GP Framework | 352 |
Experimental Comparison | 355 |
Conclusions and Future Work | 359 |
From Solomonoffs Theory to a Fitness Function | 360 |
Introduction | 361 |
The Broadcast System | 362 |
Modeling a Biochemical Network | 365 |
Discussion | 368 |
Conclusion | 369 |
Introduction | 371 |
Finite Transducers | 372 |
Proposed GP System | 373 |
Experimental Methodology | 376 |
Results and Discussion | 377 |
Conclusion | 379 |
Citi izdevumi - Skatīt visu
Genetic Programming: 10th European Conference, EuroGP 2007, Valencia, Spain ... Marc Ebner Ierobežota priekšskatīšana - 2007 |
Bieži izmantoti vārdi un frāzes
algorithm allow analysis applied approach average better binary blocks broadcast changes classifiers combination compared comparison complexity computational Conference considered contained corresponding crossover defined described distribution effect effort estimation evaluation evolution evolutionary evolved example executed experiments expression Figure fitness fragments function Genetic Programming genotype given implementation increase individuals initial input International Italy landscape language layer learning length machine mean measures method move mutation neutrality nodes Note object obtained operator output parameters particles performance population position possible present prime probability problem Proceedings produced proposed random representation represented rule runs sample scheduling selection shapes shown shows signal single sizes solution solve space standard structure subtree success Table techniques terminal tree University values variable weights
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