Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH
Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH
Operators: Selection Example: (2,3)-Selection Example: (2+3)-Selection Parents don‘t survive … Parents don‘t survive! … but a worse offspring. … now this offspring survives. 20
Operators: Selection Possible occurrences of selection Exception! (1+1)-ES: One parent, one offspring, 1/5-Rule (1,λ)-ES: One Parent, λ offspring Example: (1,10)-Strategy One step size / n self-adaptive step sizes Mutative step size control Derandomized strategy (μ,λ)-ES: μ > 1 parents, λ > μ offspring Example: (2,15)-Strategy Includes recombination Can overcome local optima (μ+λ)-strategies: elitist strategies 21
- 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: Evolution Strategy Algorithms Selec
- 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 and 40: MDO Run Comparison Initial design,
- Page 41: Corporate Headquarters: Charlotte,
Operators: Selection<br />
Example: (2,3)-Selection<br />
Example: (2+3)-Selection<br />
Parents don‘t survive …<br />
Parents don‘t survive!<br />
… but a worse offspring.<br />
… now this offspring survives.<br />
20
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