Sign inSign up
Solving Optimization Problems in Industry: An Arms Race thumbnail
Pause
Mute
Subtitles
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

Solving Optimization Problems in Industry: An Arms Race

Published on Oct 27, 20141928 Views

Thomas Bäck

Many industries use simulation tools for virtual product design, and there is a growing trend towards using simulation in combination with optimization algorithms. The requirements for optimization un

PPSN 2014 - Ljubjana

Related categories

Artificial IntelligenceRobotics

Chapter list

Solving Optimization Problems in Industry: An Arms Race00:00
What if … You Had Very Few Trials? - 100:27
What if … You Had Very Few Trials? - 202:33
MDO Crash / Statics / Dynamics03:43
MDO Production Runs (I)06:02
MDO Production Runs (II)07:24
Evolutionary Algorithms Are Now a Standard08:21
Example: PIDO-System08:42
Workflow09:15
Optimization Methods - 109:32
Optimization Methods - 209:37
Multiple Objectives, Pareto-Front10:14
Issues...10:32
What They Want …12:49
The Zoo of Evolutionary Strategies13:31
Contemporary Evolution Strategies - 113:47
Contemporary Evolution Strategies - 214:39
Contemporary Evolution Strategies - 314:56
Contemporary Evolution Strategies - 415:07
Contemporary Evolution Strategies - 515:12
Contemporary Evolution Strategies - 615:26
Contemporary Evolution Strategies - 715:37
Contemporary Evolution Strategies - 816:01
Contemporary Evolution Strategies - 916:22
Contemporary Evolution Strategies - 1016:34
Contemporary Evolution Strategies - 1116:46
Contemporary Evolution Strategies - 1216:56
Contemporary Evolution Strategies - 1317:07
Contemporary Evolution Strategies - 1417:12
Contemporary Evolution Strategies - 1517:21
Contemporary Evolution Strategies - 1617:28
Contemporary Evolution Strategies - 1717:44
Contemporary Evolution Strategies - 1817:53
Contemporary Evolution Strategies - 1917:57
Contemporary Evolution Strategies - 2018:02
Mirrored Orthogonal Sampling - 118:25
Mirrored Orthogonal Sampling - 218:35
Mirrored Orthogonal Sampling - 318:53
Mirrored Orthogonal Sampling - 419:22
Mirrored Orthogonal Sampling - 519:44
Mirrored Orthogonal Sampling - 620:28
Very Small Numbers of Function Evaluations?21:56
Therefore, the questions are:22:01
Some Results on BBOB - 122:11
Some Results on BBOB - 223:30
However...24:19
Empirical Investigation - 124:31
Efficieny Measures24:39
F1 Sphere24:48
Empirical Investigation - 225:57
F22 Gallagher 21 pks26:43
A Test Problem „Like Reality“ - 128:34
A Test Problem „Like Reality“ - 228:57
A Test Problem „Like Reality“ - 329:55
A Test Problem „Like Reality“ - 430:54
A Test Problem „Like Reality“ - 531:01
A Test Problem „Like Reality“ - 631:28
A Test Problem „Like Reality“ - 731:54
A Test Problem „Like Reality“ - 832:11
A Test Problem „Like Reality“ - 932:43
OK … Longer ES Run - 133:04
OK … Longer ES Run - 233:17
After … Lots of Work33:33
Research Project33:52
The COBRA Algorithm - 134:39
The COBRA Algorithm - 236:12
With Improved Initialization36:31
With „RepairInfeasible“: MOPTA0836:49
Additional Effort…37:14
Power Distribution Network Reconfiguration37:45
Introduction38:13
Problem Description - 138:46
Problem Description - 239:15
Problem Description - 340:55
Problem Description - 441:20
Algorithms42:00
Single Objective Optimization - 142:12
Single Objective Optimization - 242:31
Multi - Objective Optimization43:01
NACO Group44:16