Genetic Programming: 10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007, Proceedings

Pirmais vāks
Marc 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

 

Atlasītās lappuses

Saturs

Introduction
1
Background
2
Experimental Setup and Results
5
Conclusions and Future Work
10
Introduction
12
Boosting Evolutionary ROCBased Learner
15
Experiments with ROCBased GP and ES
17
Conclusion
21
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

Introduction
23
Defining Confidence Intervals
24
When Minimum Generation Is Known
26
When the Minimum Generation Is Unknown
29
Further Analysis of Wilsons Method
31
Conclusions
32
Introduction
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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Bibliogrāfiskā informācija

NosaukumsGenetic Programming: 10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007, Proceedings
Grāmatas Lecture Notes in Computer Science 4445. sējums. sējums
Theoretical Computer Science and General Issues
RedaktoriMarc Ebner, Michael O'Neill, Anikó Ekárt, Leonardo Vanneschi, Anna Isabel Esparcia-Alcázar
Izdevums:ilustrēts
IzdevējsSpringer, 2007
ISBN354071605X, 9783540716051
Apjoms382 lappuses
  
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