Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH

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Concluding Remarks ES are very useful <strong>for</strong> MDO tasks High dimensionality Few evaluations (shots) possible Mixed-integer tasks MCDM tasks 40
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Page 1 and 2: Evolutionary Strategies for Multidi
Page 3 and 4: Background I Biology = Engineering
Page 5 and 6: Optimization f : objective function
Page 7 and 8: Dynamic Optimization Dynamic Functi
Page 9 and 10: The Fundamental Challenge Global co
Page 11 and 12: Evolution Strategies 11
Page 13 and 14: Evolution Strategy - Basics Mostly
Page 15 and 16: Evolution Strategy: Algorithms Muta
Page 17 and 18: Operators: Mutation - one σ Thereb
Page 19 and 20: Evolution Strategy Algorithms Selec
Page 21 and 22: Operators: Selection Possible occur
Page 23 and 24: Self-adaptation Self adaptation No
Page 25 and 26: Self-adaptation Self adaptation: :
Page 27 and 28: Mixed-Integer Mixed Integer Evoluti
Page 29 and 30: Mixed-Integer Mixed Integer ES: Mut
Page 31 and 32: Multidisciplinary Optimization (MDO
Page 33 and 34: MDO Crash / Statics / Dynamics Mini
Page 35 and 36: MDO Production Runs (II) Mass NuTec
Page 37 and 38: MDO ASF ® Front Optimization Pre-o
Page 39: MDO Run Comparison Initial design,

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