gecco 2013, industrial challenge, 2nd place
DESCRIPTION
GECCO 2013, Industrial Challenge, 2nd place. Contact : Gabriel Kronberger Heuristic and Evolutionary Algorithms Lab (HEAL) Softwarepark 11 A-4232 Hagenberg e-mail: [email protected] Web: http://heal.heuristiclab.com http://heureka.heuristiclab.com. - PowerPoint PPT PresentationTRANSCRIPT
Gabriel Kronberger and Michael Kommenda
GECCO 2013, Industrial Challenge, 2nd place Contact: Gabriel KronbergerHeuristic and Evolutionary Algorithms Lab (HEAL)Softwarepark 11 A-4232 Hagenberg
e-mail: [email protected]: http://heal.heuristiclab.com http://heureka.heuristiclab.com
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Heuristic and Evolutionary Algorithms Research Group
Research Group• 4 professors• 7 PhD students• Various interns, Master and Bachelor students
Research Focus• Problem modeling• Process optimization• Data-based systems identification• Supply chain and logistics optimization• Algorithm development and analysis
Industry Partners (excerpt)
Scientific Partners
Kronberger – Industrial Challenge – GECCO 2013
3Kronberger – Industrial Challenge – GECCO 2013
Submission: Temperature
TrainingTest
15.274
17.274
19.274
21.274
23.274
25.274
27.274
0 200 400
Estimated Values (training) Estimated Values (test) Estimated Values (all samples) temp
4Kronberger – Industrial Challenge – GECCO 2013
Temperature: Predicted and Actual Values
19
20
21
22
23
24
25
26
27
1 18 35 52 69 86 103
120
137
154
171
188
205
222
239
256
273
290
307
324
341
358
375
392
409
426
value
pred
5Kronberger – Industrial Challenge – GECCO 2013
Submission: Humidity
TrainingTest
0
20
40
60
80
100
0 85 170 255 340 425
Estimated Values (training) Estimated Values (test) Estimated Values (all samples) humidity
6Kronberger – Industrial Challenge – GECCO 2013
Humidity: Predicted and Actual Values
0
5
10
15
20
25
30
35
1 16 31 46 61 76 91 106
121
136
151
166
181
196
211
226
241
256
271
286
301
316
331
346
361
376
391
406
421
value
pred
7Kronberger – Industrial Challenge – GECCO 2013
Method: Gaussian Processes
A Gaussian process is a distribution over functions• fully Bayesian, non-parametric, kernel-based
Learning:Given: a GP prior and observed data Result: a posterior GP = conditional probability over functions given data
training data
posterior mean +/-
posterior mean