ayu0rkuqtxmwfssjsujp topic 9

8/12/2019 AYu0rkuQTxmWFsSjsuJp Topic 9

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Using decoded values ofUsing decoded values ofxx andandyy as inputs in theas inputs in the

mathematical function, the GA calculates themathematical function, the GA calculates the

fitness of each chromosome.fitness of each chromosome.

To find the maximum of the peak function, weTo find the maximum of the peak function, we

will use crossover with the probability equal to 0.7will use crossover with the probability equal to 0.7

and mutation with the probability equal to 0.001.and mutation with the probability equal to 0.001.

As we mentioned earlier, a common practice inAs we mentioned earlier, a common practice in

GAs is to specify the number of generations.GAs is to specify the number of generations.

Suppose the desired number of generations is 100.Suppose the desired number of generations is 100.

That is, the GA will create 100 generations of 6That is, the GA will create 100 generations of 6chromosomes before stopping.chromosomes before stopping.

Chromosome locations on the surface of theChromosome locations on the surface of the

peak function: initial populationpeak function: initial population


peak function: first generationpeak function: first generation


peak function: local maximumpeak function: local maximum


peak function: global maximumpeak function: global maximum

Performance graphs for 100 generations of 6Performance graphs for 100 generations of 6

chromosomes:chromosomes: local maximumlocal maximumpc= 0.7, pm= 0.001

0.5

0.6

0.7

0.4

G e n e r a t i o n s

Best

Average

80 90 10060 7040 5020 30100-0.1

Fitness

0

0.1

0.2

0.3


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Fitness history of the best SFitness history of the best S--expressionexpression

ss,%

60

80

100

sqrt

+

Fitne

0 1 2 3 40

20

40

a

*

a b b

*

G e n e r a t i o n s B e s t o f g e n e r a t i o n

What are the main advantages of geneticWhat are the main advantages of genetic

programming compared to genetic algorithms?programming compared to genetic algorithms?

Genetic programming applies the sameGenetic programming applies the same

evolutionary approach. However, geneticevolutionary approach. However, genetic

programming is no longer breeding bit strings thatprogramming is no longer breeding bit strings that

represent coded solutions but complete computerrepresent coded solutions but complete computer

programs that solve a particular problem.programs that solve a particular problem.

The fundamental difficulty of GAs lies in theThe fundamental difficulty of GAs lies in the

problem representation, that is, in the fixedproblem representation, that is, in the fixed--lengthlength

coding. A poor representation limits the power ofcoding. A poor representation limits the power ofa GA, and even worse, may lead to a falsea GA, and even worse, may lead to a false

solution.solution.

A fixedA fixed--length coding is rather artificial. As itlength coding is rather artificial. As it

cannot provide a dynamic variability in length,cannot provide a dynamic variability in length,

such a coding often causes considerablesuch a coding often causes considerable

redundancy and reduces the efficiency of geneticredundancy and reduces the efficiency of genetic

search. In contrast, genetic programming usessearch. In contrast, genetic programming uses

highhigh--level building blocks of variable length.level building blocks of variable length.

Their size and complexity can change duringTheir size and complexity can change during

breeding.breeding.

Genetic programming works well in a largeGenetic programming works well in a large

number of different cases and has many potentialnumber of different cases and has many potentialapplications.applications.

ayu0rkuqtxmwfssjsujp topic 9

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