Transcript
Dr gafar zen alabdeen salh (2011) 1
Evolutionary Computation: Genetic algorithms
Can evolution be intelligent?
2Dr gafar zen alabdeen salh (2011)
evolutionary
computationgenetic algorithms
selection
(mutation)reproduction3Dr gafar zen alabdeen salh (2011)
Simulation of natural evolution
Charles Darwin
Mendal
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reproduction
, mutationcompetition
selection
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evoluation fitness
ecologymorphology
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How is a population with increasing fitness generated?
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Simulation of natural evolution
Genetic
Algorithms
GAs
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Genetic Algorithms
1 10 1 0 1 0 0 0 0 0 1 0 1 10
GA
crossovermutation
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GAsGA
encoding.
evaluation
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GAs
GA
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genetic algorithms
GAGA
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Basic genetic algorithms
Npc
crossover probabilitypm.
mutation probability
fitness function
N
x1, x2 , . . . , xN
f (x1), f (x2), . . . , f (xN) 13Dr gafar zen alabdeen salh (2011)
Basic genetic algorithms
–
N14Dr gafar zen alabdeen salh (2011)
Basic genetic algorithms
GA
generationGA
RUN
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GA
GA
GA
X
X
f(x) = 15 x – x2
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Genetic algorithms: case study
NPC
PM
PCPMGAs
Integer Binary code Integer Binary code Integer Binary code
1 11
2 7 12
3 8 13
4 9 14
5 10 15
6 1 0 1 1
1 1 0 0
1 1 0 1
1 1 1 0
1 1 1 1
0 1 1 0
0 1 1 1
1 0 0 0
1 0 0 1
1 0 1 0
0 0 0 1
0 0 1 0
0 0 1 1
0 1 0 0
0 1 0 1
f(x) = 15 x – x2
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18Dr gafar zen alabdeen salh (2011)
The fitness function and chromosome locations
Chromosome
label
Chromosome
string
Decoded
integer
Chromosome
fitness
Fitness
ratio, %
X1 1 1 0 0 12 36 16.5
X2 0 1 0 0 4 44 20.2
X3 0 0 0 1 1 14 6.4
X4 1 1 1 0 14 14 6.4
X5 0 1 1 1 7 56 25.7
X6 1 0 0 1 9 54 24.8
x
50
40
30
20
60
10
00 5 10 15
f(x)
(a) Chromosome initial locations.
x
50
40
30
20
60
10
00 5 10 15
(b) Chromosome final locations.
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x5x6
x3x4
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Roulette wheel selection
Roulette wheel selection
100 0
36.743.149.5
75.2
X1: 16.5%
X2: 20.2%
X3: 6.4%
X4: 6.4%
X5: 25.3%
X6: 24.8%
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[0,100]
x6x2
x1x5
x2x5
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Crossover operatorbreak
x6x2
cloning
x2x5
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Crossover
X6i 1 00 0 01 0 X2i
0 01 0X2i 0 11 1 X5i
0X1i 0 11 1 X5i1 01 0
0 10 0
11 101 0
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25Dr gafar zen alabdeen salh (2011)
Mutation operator
Mutation
0 11 1X5'i 01 0
X6'i 1 00
0 01 0X2'i 0 1
0 0
0 1 111X5i
1 1 1 X1"i1 1
X2"i0 1 0
0X1'i 1 1 1
0 1 0X2i
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Mutation operator
x2
GAs
GA
Near-optimal
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The genetic algorithm cycle
1 01 0X1i
Generation i
0 01 0X2i
0 00 1X3i
1 11 0X4i
0 11 1X5i f = 56
1 00 1X6i f = 54
f = 36
f = 44
f = 14
f = 14
1 00 0X1i+1
Generation (i + 1)
0 01 1X2i+1
1 10 1X3i+1
0 01 0X4i+1
0 11 0X5i+1 f = 54
0 11 1X6i+1 f = 56
f = 56
f = 50
f = 44
f = 44
Crossover
X6i 1 00 0 01 0 X2i
0 01 0X2i 0 11 1 X5i
0X1i 0 11 1 X5i1 01 0
0 10 0
11 101 0
Mutation
0 11 1X5'i 01 0
X6'i 1 00
0 01 0X2'i 0 1
0 0
0 1 111X5i
1 1 1 X1"i1 1
X2"i0 1 0
0X1'i 1 1 1
0 1 0X2i
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The End
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