genetic algorithms lecture 5 addendum to the slides of jason noble
TRANSCRIPT
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Genetic Algorithms
Lecture 5
Addendum to the slides of Jason Noble
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Evolutionary Algorithms
NN II
NI
N I
NI
N I N I
NN NI NI II
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Evolutionary Algorithms
Binary Genetic Algorithms
Variable Encoding and Decoding Generating a Population Generating Offspring:
Crossover Mutation Selection
Real chromosomes diploid GA almost always (haploid) exception will be
explicitly mentioned
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Evolutionary Algorithms
Example (Haupt & Haupt, 2004)
Suppose we have a digital map of a mountain range
We want to find the highest peak on the map Automatically! Difficult problem for conventional optimization
techniques
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Evolutionary Algorithms
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Evolutionary Algorithms
Variable EncodingVariable Value Decimal Binary
Latitude
(min)
40o,15’ 1 0000000
Latitude
(max)
40o,16’ 128 1111111
Longitude
(min)
105036’ 1 0000000
Longitude
(max)
105037’30’’ 128 1111111
Chromosome =[11000110011001]
X y
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Evolutionary Algorithms
Generate an Initial Population
00101111000110 12359
11100101100100 11872
00110010001100 13477
00101111001000 12363
11001111111011 11631
01000101111011 12097
11101100000001 12588
01001101110011 11860
Assign each chromosome a fitness (in this case simply the height of the point)
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Evolutionary Algorithms
Selection (at 50% selection rate)
00101111000110 12359
00110010001100 13477
00101111001000 12363
11101100000001 12588
Selection:•Choose a selection rate•Choose a threshold (all above survive)
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Evolutionary Algorithms
Assume random mating
00101111000110 12359
00110010001100 13477
00101111001000 12363
11101100000001 12588
Selection:•Choose a selection rate•Choose a threshold (all above survive)
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Evolutionary Algorithms
Assume random mating
00101111000110 12359
00110010001100 13477
00101111001000 12363
11101100000001 12588
Selection:•Choose a selection rate•Choose a threshold (all above survive)
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Evolutionary Algorithms
Crossover
00101111000110
00110010001100
00101111001000
11101100000001
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Evolutionary Algorithms
Crossover
00101111000110
00110010001100
00101111001000
11101100000001
Crossover point
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Evolutionary Algorithms
Crossover
00101111000110
00110010001100
00101111001000
11101100000001
Crossover point
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Evolutionary Algorithms
Crossover
00101111000110
00110010001100
00101111001000
11101100000001
Crossover point
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Evolutionary Algorithms
Crossover
00101111000110 00101100000001
00110010001100
00101111001000
11101100000001 11101111000110
Crossover point
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Evolutionary Algorithms
Crossover
00101111000110 00101100000001
00110010001100
00101111001000
11101100000001 11101111000110
Crossover point
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Evolutionary Algorithms
Crossover
00101111000110 00101100000001
00110010001100 00110111001000
00101111001000 00101010001100
11101100000001 11101111000110
Crossover point
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Evolutionary Algorithms
New generation (almost)
00101111000110 00101100000001
00110010001100 00110111001000
00101111001000 00101010001100
11101100000001 11101111000110
Crossover point
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Evolutionary Algorithms
Mutation
00101111000110 00101100000001
00110010101100 00110111001000
00101111001000 00101010101100
11101100000001 11101111000110
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Evolutionary Algorithms
Over the generations ….
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Evolutionary Algorithms
Summary: Canonical Genetic Algorithm
S1: Set t= 0 S2: Initialize chromosome population P(t)
Usually by random init. S3: Evaluate P(t) by a fitness measure S4: while (termination not satisfied) do:
begin S4.1 Select for recombination chromosomes from P(t). Let P1
be the set of selected chromosomes. Choose individuals from P1 to enter the mating pool (MP)
S4.2 Recombine the chromosomes in MP forming P2. Mutate chromosomes in P2 forming P3
S4.3 Select for replacement from P3 and P(t) forming P(t+1) t = t + 1end
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Evolutionary Algorithms
Generating offspring
Weighted random pairing (roulette wheel weighting)
Rank weighting Cost weighting
Equivalently allow a number of copies to mate (weighted)
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Evolutionary Algorithms
Generating offspring
Roulette wheel selection How many copies take part in mating?
Rank weighting Cost weighting
http://www.edc.ncl.ac.uk/highlight/rhjanuary2007g02.php/
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Evolutionary Algorithms
Roulette wheel weighting (1)
00110010001100 13477
11101100000001 12588
00101111000110 12359
00101111001000 12363
0.4 0.4
0.3 0.7
0.2 0.9
0.1 1.0
Rank weighting: assign distributions by rank, make sure that total probability is 1.0
pnΣpn
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Evolutionary Algorithms
Roulette wheel weighting (2)
00110010001100 13477
11101100000001 12588
00101111001000 12363
00101111000110 12359
0.265 0.265
0.248 0.513
0.243 0.756
0.243 1.0
Fitness based weighting: assign distributions by fitness, make sure that total probability is 1.0
pnΣpn
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Evolutionary Algorithms
Roulette wheel weighting (3)
00110010001100 13477-12097
= 1380
11101100000001 12588-12097 = 491
00101111001000 12363-12097
= 266
00101111000110 12359-12097 = 262
0.575 0.575
0.205 0.780
0.111 0.891
0.109 1.0
Cost based weighting: assign distributions by cost, make sure that total probability is 1.0
pnΣpn
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Evolutionary Algorithms
Pairing offspring
Randomly Rank based (1-2) (3-4)
Prevents overly quick convergence Tournament:
Pick two chromosomes Set parameter k Generate random nr r If (r < k) pick fitter (eg. k = 0.75)
Elitism: always pick (a couple of) the best
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Evolutionary Algorithms
Real valued chromosomes
In case of example: (x, y), where x and y are the spatial
coordinates Crossover
Discrete recombination Intermediate recombination Line recombination
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Evolutionary Algorithms
Real valued chromosomesCrossover
Line recombination (122, 45, 77) and (3,4,3) Generate random positions:
(1,2,2) and (1,1,2) Results in:
(122, 4, 3) and (122, 45, 3)
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Evolutionary Algorithms
Real valued chromosomes
Intermediate recombination Offspring = parent 1 + α(parent 2 – parent 1) Allow area to be slightly larger than hypercube
defined by parents Generate α for each position
Line recombination Use a single value for α
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Evolutionary Algorithms
Genetic Programming(Koza, 1992,1994,…)
Example (Mitchell, 1998): Kepler’s law P2 = cA3, P orbital Period, A average distance
from sun
Programme for Mars:
Program Orb
// Mars
A = 1.52;
P = SQRT(A*A*A);
PRINT P
END
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Evolutionary Algorithms
Genetic Programming
Lisp version(defun orbital_period ()
; Mars;
(setf A 1.52)
(sqrt (A A (* A A))))
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Evolutionary Algorithms
Parse tree for LISP expression
SQRT
*
A *
A A
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Evolutionary Algorithms
Koza’s algorithm
Trees consist of functions and terminals Choose a set of functions and terminals, e.g
{ +, -, *, /, √}; {A} Generate random programmes (trees) which
are syntactically correct Evaluate fitness Apply crossover (mutation)
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Evolutionary Algorithms
Crossover
/
A /
/ /
A A A A
*
A -
* √
A A A
/
A /
/ /
√ A A A
*
A -
* A
A A
X
A