Evolutionary Strategies for Multidisciplinary ... - Dynardo GmbH

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Generalized <strong>Evolutionary</strong> Algorithm t := 0; initialize(P(t)); evaluate(P(t)); while not terminate do od P‘(t) := mating_selection(P(t)); P‘‘(t) := variation(P‘(t)); evaluate(P‘‘(t)); P(t+1) := environmental_selection(P‘‘(t) ∪ Q); t := t+1; 12
Evolution Strategy – Basics Mostly real-valued search space IR n also mixed-integer, discrete spaces Emphasis on mutation n-dimensional normal distribution expectation zero Different recombination operators Deterministic selection (μ, λ)-selection: Deterioration possible (μ+λ)-selection: Only accepts improvements λ >> μ, i.e.: Creation of offspring surplus Self-adaptation of strategy parameters. 13
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: Evolution Strategies 11
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 and 40: MDO Run Comparison Initial design,
Page 41: Corporate Headquarters: Charlotte,

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