Genetic Algorithm for Variable Selection by Jennifer

7/25/2019 Genetic Algorithm for Variable Selection by Jennifer

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Genetic Algorithmfor Variable Selection

Jennifer Pittman

ISDSDuke University


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Genetic AlgorithmsStep by Step

Jennifer Pittman

ISDSDuke University


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Example:Protein Signature Selection in Mass Spectrometry

http

://www.uni-mainz.d

e/~frosc/f!g"po#.h

tml

molecular weight

relati$eintensity


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%enetic &lgorithm '(olland)

*heuristic method !ased on +sur$i$al of the fittest,

*in each iteration 'generation) possi!le solutions or indi$iduals represented as strings of num!ers

*useful when search space$ery largeor toocomplexfor analytic treatment

# #0 #1


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2lowchart of %&

3h

ttp://www.spe

ctroscopynow.com

*indi$iduals allowed toreproduce'selection)4crosso$er4mutate

*all indi$iduals inpopulatione$aluated !yfitness function


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http://i!-poland.$

irtuala$e.net/e

e/genetic/#geneticalgorithms.h

tm


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5nitialization

*proteins corresponding to 6 mass spectrometry$alues from #-# m/z

*assume optimal signature contains # peptides represented !y their m/z $alues in!inary encoding

*population size~M78/ where8 is signature length

'a simplified example)


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5nitialPopulation

M 7

8 7 1


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Searching

*search space defined !y all possi!le encodings ofsolutions

*selection4crosso$er4 and mutation perform+pseudo-random, wal9 through search space

*operations arenon-deterministicyetdirected


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Phenotype istri!ution

http://www.ifs.tuwien.ac.at/~aschatt/info/ga/genetic.html


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E$aluation and Selection

*e$aluate fitness of each solution in current

population 'e.g.4 a!ility to classify/discriminate)

;in$ol$es genotype-phenotype decoding


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=oulette >heel Selection3http://www.softchitech.com/ec"intro"html


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?rosso$er

*com!ine two indi$iduals to create new indi$iduals

for possi!le inclusion innext generation

*main operator for local search 'loo9ing close toexisting solutions)

*perform each crosso$er with pro!a!ility pc @.4A4.0B

*crosso$erpoints selected at random

*indi$iduals not crossed carried o$er in population


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5nitial Strings Cffspring

Single-Point

Dwo-Point

niform


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Mutation

*each component of e$ery indi$idual is modified with

pro!a!ility pm

*main operator for glo!al search 'loo9ing at newareas of the search space)

*indi$iduals not mutated carried o$er in population

*pm usually small @.4A4.B

rule of thum! 7 /no. of !its in chromosome


16/273http://www.softchitech.com/ec"intro"html


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# #0 #1

#F #G #6

##6 #0 #

#GF #00 #6

.6F

.#

.1

.G1

phenotype genotype fitness

1

#

#

1

1

selection


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one-point crosso$er 'p7.6)

.#

.0

mutation 'p7.)


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# #0 #1

#F #G #6##6 #0 #

#GF #00 #6

.6F

.#.1

.G1

starting generation

next generation

phenotypegenotype fitness

# #1G #

#66 #01 #1

#GF # #6

## #00 #1

.0

.FF.1

.G0


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1 6 0

%& E$olution

%enerations

&ccuracy

inPercent

http://www.sdsc.edu/s9idl/proHects/!io-SI58/


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genetic algorithm learning

http://www.demon.co.u9/apl#0/aplG6/s9om.htm

-F

-6

-

-1

%enerations

2itness

criteria


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2itness$a

lue'scaled)

iteration


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*(olland4 J. 'GG)4 &daptation in natural andartificial systems4 ndEd. ?am!ridge: M5D Press.

*a$is4 8. 'Ed.) 'GG)4 (and!oo9 of genetic algorithms.Kew Lor9: an Kostrand =einhold.

*%old!erg4 . 'G0G)4 %enetic algorithms in search4optimization and machine learning. &ddison->esley.

References

*2ogel4 . 'GG)4 E$olutionary computation: Dowards anew philosophy of machine intelligence. Piscataway:5EEE Press.

*NOc94 D.4 (ammel4 .4 and Schwefel4 (. 'GGF)4+E$olutionary computation: ?omments on the history andthe current state,4 5EEE Drans. Cn E$ol. ?omp. 4 ')


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*http://www.spectroscopynow.com

*http://www.cs.!ris.ac.u9/~colin/e$ollect/e$ollect/index.htm

*5lli%&8 'http://www-illigal.ge.uiuc.edu/index.php#)

nline Resources

*%&li! 'http://lancet.mit.edu/ga/)


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iteration

Perc

entimpro$em

ento$erhillclim!er


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Schema and %&s

*aschema is template representing set of !it strings @ 4 4 4 4 A B

*e$ery schema s has an estimated a$erage fitness f's):

EtQ 9;f's)/f'pop)

Documents

Genetic Algorithm for Variable Selection by Jennifer