# Genetic Algorithms Genetic algorithms imitate natural optimization process, natural selection in evolution. Developed by John Holland at the University.

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Genetic Algorithms Genetic algorithms imitate natural optimization process, natural selection in evolution. Developed by John Holland at the University of Michigan for machine learning in 1975. Similar algorithms developed in Europe in the 1970s under the name evolutionary strategies Main difference has been in the nature of the variables: Discrete vs. continuous Class is called evolutionary algorithms Will cover also differential evolution.

Basic Scheme Coding: replace design variables with a continuous string of digits or genes –Binary –Integer –Real Population: Create population of design points Selection: Select parents based on fitness Crossover: Create child designs Mutation: Mutate child designs

Genetic operators Crossover: portions of strings of the two parents are exchanged Mutation: the value of one bit (gene) is changed at random Permutation: the order of a portion of the chromosome is reversed Addition/deletion: one gene is added to/removed from the chromosome

Select parents Algorithm of standard GA Create initial population Calculate fitness 40 100 30 70 Create children

Coding Integer variables are easily coded as they are or converted to binary digits Real variables require more care Key question is resolution or interval The number m of required digits found from

Stacking sequence optimization For many practical problems angles limited to 0-deg, 45-deg, 90-deg. Ply thickness given by manufacturer Stacking sequence optimization a combinatorial problem Genetic algorithms effective and easy to implement, but do not deal well with constraints

Coding - stacking sequence Natural coding: (0 0 =>1, 45 0 =>2, - 45 0 =>3, 90 0 =>4) (45/-45/90/0)s => (2/3/4/1) To satisfy balance condition, convenient to work with two-ply stacks (0 2 =>1, 45=>2, 90 2 =>3) or (45/-45/90 2 /0 2 ) s => (2/3/1) To allow variable thickness add empty stacks (2/3/1/E/E)=> (45/-45/90 2 /0 2 ) s

Initial population Random number generator used Typical function call is rand(seed) Seed updated after call to avoid repeating the same number. See Matlab help on how to change seed (state). Need to transform random numbers to values of alleles.

Fitness Augmented objective f*=f + pv-bm+sign(v). –v = max violation –m = min margin Repair may be more efficient than penalty Fitness is normalized objective or n s +1-rank What is the advantage of rank based objective?

Roulette wheel selection Example fitnesses

Single Point Crossover Parent designs [0 4 /±45 2 /90 2 ] s and [±45 4 /0 2 ] s Parent 1 [1/1/2/2/ 3] Parent 2 [2/2/2/2/ 1] One child [1/1/2/2/1] That is: [0 4 /±45 2 /0 2 ] s

Other kinds of crossover Multiple point crossover Hitchhiking problem Uniform crossover Random crossover for real numbers Multi-parent crossover

Mutation and stack swap [1/1/2/2/3]=> [1/1/2/3/3] [0 4 /±45 2 /90 2 ] s => [0 4 /±45/90 4 ] s [1/1/2/2/3]=> [1/2/1/2/3] [0 4 /±45 2 /90 2 ] s => [(0 2 /±45) 2 /90 2 ] s

Differential evolution (Wikipedia) Initialize m designs with n real number Repeat the following: Crossover: For each design x –find three other random unique designs a,b,c to combine with y=y(x,a,b,c). –For each design variable make a decision based on random number whether to leave alone or combine. Replacement: If y is better than x replace the x with y.

Combination details

Questions Global optimization balances exploration and exploitation. How is that reflected in genetic algorithms? What are all possible child designs of [0 2 /±45/90] s and [±45 2 /0] s that are balanced and symmetric? When we breed plants and animals we do not introduce randomness on purpose into the selection procedure. Why do we do that with GAs?

Reliability Genetic algorithm is random search with random outcome. Reliability r can be estimated from multiple runs for similar problems with known solution Variance of reliability, r, from n runs

Reliability curves

Multi-material laminate Materials: one material = 1 lamina ( matrix or fiber materials) E.g.: glass-epoxy, graphite-epoxy, Kevlar-epoxy… Use two materials in order to combine high efficiency (stiffness) and low cost Graphite-epoxy: very stiff but expensive; glass-epoxy: less stiff, less expensive Objective: use graphite-epoxy only where most efficient, use glass-epoxy for the remaining plies

Multi-criterion optimization Two competing objective functions: WEIGHT and COST Design variables: –number of plies –ply orientations –ply materials No single design minimizes weight and cost simultaneously: A design is Pareto-optimal if there is no design for which both Weight and Cost are lower Goal: construct the trade-off curve between weight and cost (set of Pareto-optimal designs)

Material properties Graphite-epoxy –Longitudinal modulus, E 1 : 20.01 10 6 psi –Transverse modulus, E 2 : 1.30 10 6 psi –Shear modulus, G 12 : 1.03 10 6 psi –Poisson s ratio, 12 : 0.3 –Ply thickness, t: 0.005 in –Density, : 5.8 10 -2 lb/in 3 –Ultimate shear strain, ult : 1.5 10 -2 –Cost index: \$8/lb Glass-epoxy –Longitudinal modulus, E 1 : 6.30 10 6 psi –Transverse modulus, E 2 : 1.29 10 6 psi –Shear modulus, G 12 : 6.60 10 5 psi –Poisson s ratio, 12 : 0.27 –Ply thickness, t: 0.005 in –Density, : 7.2 10 -2 lb/in 3 –Ultimate shear strain, ult : 2.5 10 -2 –Cost index: \$1/lb

Material properties Carbon-epoxyGlass-epoxy E 1 (psi)20.01 x 10 6 5.7 x 10 6 E 2 (psi)1.30 x 10 6 1.24 x 10 6 G 12 (psi)1.03 x 10 6 0.54 x 10 6 12 0.30.28 (lb/in 3 ) 0.0580.076 Cost (lb -1 )81-2 Thickness (in)0.005 1 lim 0.010.02 2 lim 0.010.02 12 lim 0.0150.025 Source: http://composite.about.com for the stiffnesses, Poisson's ratios and densitieshttp://composite.about.com

Method for constructing the Pareto trade-off curve Simple method: weighting method. A composite function is constructed by combining the 2 objectives: W: weight C: cost : weighting parameter (0 1) A succession of optimizations with varying from 0 to 1 is solved. The set of optimum designs builds up the Pareto trade-off curve

Multi-material Genetic Algorithm Two variables for each ply: –Fiber orientation –Material Each laminate is represented by 2 strings: –Orientation string –Material string Example: [45/0/30/0/90] is represented by: –Orientation:45-0-30-0-90 –Material: 2-2-1-2-1 GA maximizes fitness: Fitness = -F 1: graphite- epoxy 2: glass-epoxy

Simple vibrating plate problem Minimize the weight (W) and cost (C) of a 36x30 rectangular laminated plate 19 possible ply angles from 0 to 90 in 5- degree step Constraints: –Balanced laminate (for each + ply, there must be a - ply in the laminate) –first natural frequency > 25 Hz Frequency calculated using Classical Lamination Theory

How constraints are enforced GAs do not permit constrained optimization Balance constraint hard coded in the strings: stacks of ± are used Example: (45-0-30-25-90) represents [±45/0/±30/±25/90] s Other constraints (frequency, maximum strain…) are incorporated into the objective function by a penalty, which is proportional to the constraint violation >0: penalty parameter, g: constraint

Pareto Trade-off curve (\$) (lb) A (16.3,16.3) B (5.9,55.1) C point C –64% lighter than A; 17% more expensive –53% cheaper than B; 25% heavier

Optimum laminates Cost minimization: [±50 10 /0] s, cost = 16.33, weight = 16.33 Weight minimization: [±50 5 /0] s, cost = 55.12, weight = 6.89 Intermediate design: [±50 2 /±50 5 ] s, cost = 27.82, weight = 10.28 Glass-epoxy in the core layers to increase the thickness Graphite-epoxy as outer plies for a high frequency Midplane Intermediate optimum laminates: sandwich-type laminates

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