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Lecture 9
Evolutionary Computation:
Genetic algorithms
 Introduction, or can evolution be
intelligent?
 Simulation of natural evolution
 Genetic algorithms
 Case study: maintenance scheduling with
genetic algorithms
 Summary
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Can evolution be intelligent?
 Intelligence can be defined as the capability of a
system to adapt its behavior to ever-changing
environment. According to Alan Turing, the form
or appearance of a system is irrelevant to its
intelligence.
 Evolutionary computation simulates evolution on a
computer. The result of such a simulation is a
series of optimization algorithms, usually based on
a simple set of rules. Optimization iteratively
improves the quality of solutions until an optimal,
or at least feasible, solution is found.
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 The behavior of an individual organism is an
inductive inference about some yet unknown
aspects of its environment. If, over successive
generations, the organism survives, we can say
that this organism is capable of learning to predict
changes in its environment.
 The evolutionary approach is based on
computational models of natural selection and
genetics. We call them evolutionary
computation, an umbrella term that combines
genetic algorithms, evolution strategies and
genetic programming.
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Simulation of natural evolution
 On 1 July 1858, Charles Darwin presented his
theory of evolution before the Linnean Society of
London. This day marks the beginning of a
revolution in biology.
 Darwin’s classical theory of evolution, together
with Weismann’s theory of natural selection and
Mendel’s concept of genetics, now represent the
neo-Darwinian paradigm.
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 Neo-Darwinism is based on processes of
reproduction, mutation, competition and selection.
The power to reproduce appears to be an essential
property of life. The power to mutate is also
guaranteed in any living organism that reproduces
itself in a continuously changing environment.
Processes of competition and selection normally
take place in the natural world, where expanding
populations of different species are limited by a
finite space.
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 Evolution can be seen as a process leading to the
maintenance of a population’s ability to survive
and reproduce in a specific environment. This
ability is called evolutionary fitness.
 Evolutionary fitness can also be viewed as a
measure of the organism’s ability to anticipate
changes in its environment.
 The fitness, or the quantitative measure of the
ability to predict environmental changes and
respond adequately, can be considered as the
quality that is optimized in natural life.
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How is a population with increasing
fitness generated?
 Let us consider a population of rabbits. Some
rabbits are faster than others, and we may say that
these rabbits possess superior fitness, because they
have a greater chance of avoiding foxes, surviving
and then breeding.
 If two parents have superior fitness, there is a good
chance that a combination of their genes will
produce an offspring with even higher fitness.
Over time the entire population of rabbits becomes
faster to meet their environmental challenges in the
face of foxes.
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Simulation of natural evolution
 All methods of evolutionary computation simulate
natural evolution by creating a population of
individuals, evaluating their fitness, generating a
new population through genetic operations, and
repeating this process a number of times.
 We will start with Genetic Algorithms (GAs) as
most of the other evolutionary algorithms can be
viewed as variations of genetic algorithms.
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Genetic Algorithms
 In the early 1970s, John Holland introduced the
concept of genetic algorithms.
 His aim was to make computers do what nature
does. Holland was concerned with algorithms
that manipulate strings of binary digits.
 Each artificial “chromosomes” consists of a
number of “genes”, and each gene is represented
by 0 or 1:
1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 1
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 Nature has an ability to adapt and learn without
being told what to do. In other words, nature
finds good chromosomes blindly. GAs do the
same. Two mechanisms link a GA to the problem
it is solving: encoding and evaluation.
 The GA uses a measure of fitness of individual
chromosomes to carry out reproduction. As
reproduction takes place, the crossover operator
exchanges parts of two single chromosomes, and
the mutation operator changes the gene value in
some randomly chosen location of the
chromosome.
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Basic genetic algorithms
Step 1: Represent the problem variable domain as
a chromosome of a fixed length, choose the size
of a chromosome population N, the crossover
probability pc and the mutation probability pm.
Step 2: Define a fitness function to measure the
performance, or fitness, of an individual
chromosome in the problem domain. The fitness
function establishes the basis for selecting
chromosomes that will be mated during
reproduction.
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Step 3: Randomly generate an initial population of
chromosomes of size N:
x1, x2 , . . . , xN
Step 4: Calculate the fitness of each individual
chromosome:
f (x1), f (x2), . . . , f (xN)
Step 5: Select a pair of chromosomes for mating
from the current population. Parent
chromosomes are selected with a probability
related to their fitness.
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Step 6: Create a pair of offspring chromosomes by
applying the genetic operators - crossover and
mutation.
Step 7: Place the created offspring chromosomes
in the new population.
Step 8: Repeat Step 5 until the size of the new
chromosome population becomes equal to the
size of the initial population, N.
Step 9: Replace the initial (parent) chromosome
population with the new (offspring) population.
Step 10: Go to Step 4, and repeat the process until
the termination criterion is satisfied.
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Genetic algorithms
 GA represents an iterative process. Each iteration is
called a generation. A typical number of generations
for a simple GA can range from 50 to over 500. The
entire set of generations is called a run.
 Because GAs use a stochastic search method, the
fitness of a population may remain stable for a
number of generations before a superior chromosome
appears.
 A common practice is to terminate a GA after a
specified number of generations and then examine
the best chromosomes in the population. If no
satisfactory solution is found, the GA is restarted.
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Genetic algorithms: case study
A simple example will help us to understand how
a GA works. Let us find the maximum value of
the function (15x - x2) where parameter x varies
between 0 and 15. For simplicity, we may
assume that x takes only integer values. Thus,
chromosomes can be built with only four genes:
Integer
1
2
3
4
5
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Binary code
0001
0010
0011
0100
0101
Integer
6
7
8
9
10
Binary code
0110
0111
1000
1001
1010
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Integer
11
12
13
14
15
Binary code
1011
1100
1101
1110
1111
15
Suppose that the size of the chromosome population
N is 6, the crossover probability pc equals 0.7, and
the mutation probability pm equals 0.001. The
fitness function in our example is defined by
f(x) = 15 x – x2
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The fitness function and chromosome locations
Chromosome
label
Chromosome
string
X1
X2
X3
X4
X5
X6
f(x)
1
0
0
1
0
1
1
1
0
1
1
0
00
00
01
10
11
01
Decoded
integer
Chromosome
fitness
Fitness
ratio, %
12
4
1
14
7
9
36
44
14
14
56
54
16.5
20.2
6.4
6.4
25.7
24.8
60
60
50
50
40
40
30
30
20
20
10
10
0
0
5
10
15
x
(a) Chromosome initial locations.
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0
5
0
10
15
x
(b) Chromosome final locations.
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 In natural selection, only the fittest species can
survive, breed, and thereby pass their genes on to
the next generation. GAs use a similar approach,
but unlike nature, the size of the chromosome
population remains unchanged from one
generation to the next.
 The last column in Table shows the ratio of the
individual chromosome’s fitness to the
population’s total fitness. This ratio determines
the chromosome’s chance of being selected for
mating. The chromosome’s average fitness
improves from one generation to the next.
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Roulette wheel selection
The most commonly used chromosome selection
techniques is the roulette wheel selection.
100 0
75.2
36.7
49.5
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X1: 16.5%
X2: 20.2%
X3: 6.4%
X4: 6.4%
X5: 25.3%
X6: 24.8%
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Crossover operator
 In our example, we have an initial population of 6
chromosomes. Thus, to establish the same
population in the next generation, the roulette
wheel would be spun six times.
 Once a pair of parent chromosomes is selected,
the crossover operator is applied.
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 First, the crossover operator randomly chooses a
crossover point where two parent chromosomes
“break”, and then exchanges the chromosome
parts after that point. As a result, two new
offspring are created.
 If a pair of chromosomes does not cross over,
then the chromosome cloning takes place, and the
offspring are created as exact copies of each
parent.
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Crossover
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X6i
1 0 00 1
0 1 00 00 X2i
X1i
10 11 00 00
0 11 11 11 X5i
X2i
0 1 0 0
0 1 1 1 X5i
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Mutation operator
 Mutation represents a change in the gene.
 Mutation is a background operator. Its role is to
provide a guarantee that the search algorithm is
not trapped on a local optimum.
 The mutation operator flips a randomly selected
gene in a chromosome.
 The mutation probability is quite small in nature,
and is kept low for GAs, typically in the range
between 0.001 and 0.01.
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Mutation
X6'i 1 0 0 0
X2'i 0 1 0 10
X1'i 10
1 1
1
1 1 X1"i
X5'i 0 1 01 01
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X2i
0 1
X5i
0 1 1 1
0
0 1
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0 X2"i
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The genetic algorithm cycle
C rossover
Generation i
X1i
1 1 0 0
f = 36
X2i
0 1 0 0
f = 44
X3i
0 0 0 1
f = 14
X4i
1 1 1 0
f = 14
X5i
0 1 1 1
f = 56
X6i
1 0 0 1
f = 54
X6i
1 0 00 1
0 1 00 00 X2i
X1i
0 11 00 00
1
0 11 11 11 X5i
X2i
0 1 0 0
0 1 1 1 X5i
Generation (i + 1)
X1i+1 1 0 0 0 f = 56
X2i+1 0 1 0 1 f = 50
X3i+1 1 0 1 1 f = 44
X4i+1 0 1 0 0 f = 44
X5i+1 0 1 1 0 f = 54
X6i+1 0 1 1 1 f = 56
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Mutation
X6'i 1 0 0 0
X2'i 0 1 0 1
0
X1'i 1
0
1 1
1
1 1 X1"i
X5'i 0 1 0
1 0
1
X2i
0 1
X5i
0 1 1 1
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0
0 1
0 X2"i
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Genetic algorithms: case study
 Suppose it is desired to find the maximum of the
“peak” function of two variables:
2 - x 2 -( y 1) 2
f ( x, y)  (1 - x) e
- (x - x - y ) e
3
3
- x2 - y 2
where parameters x and y vary between -3 and 3.
 The first step is to represent the problem variables
as a chromosome - parameters x and y as a
concatenated binary string:
1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 1
x
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y
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 We also choose the size of the chromosome
population, for instance 6, and randomly generate
an initial population.
 The next step is to calculate the fitness of each
chromosome. This is done in two stages.
 First, a chromosome, that is a string of 16 bits, is
partitioned into two 8-bit strings:
1 0 0 0 1 0 1 0
and
0 0 1 1 1 0 1 1
 Then these strings are converted from binary
(base 2) to decimal (base 10):
(10001010 ) 2  1 27  0  26  0  2 5  0  2 4  1  23  0  2 2  1  21  0  2 0  (138 )10
and
(00111011 ) 2  0  2 7  0  2 6  1  25  1  2 4  1  2 3  0  2 2  1 21  1  2 0  (59)10
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 Now the range of integers that can be handled by
8-bits, that is the range from 0 to (28 - 1), is
mapped to the actual range of parameters x and y,
that is the range from -3 to 3:
6
 0.0235294
256 - 1
 To obtain the actual values of x and y, we multiply
their decimal values by 0.0235294 and subtract 3
from the results:
x  (138)10  0.0235294- 3  0.2470588
and
y  (59)10  0.0235294 - 3  -1.6117647
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 Using decoded values of x and y as inputs in the
mathematical function, the GA calculates the
fitness of each chromosome.
 To find the maximum of the “peak” function, we
will use crossover with the probability equal to 0.7
and mutation with the probability equal to 0.001.
As we mentioned earlier, a common practice in
GAs is to specify the number of generations.
Suppose the desired number of generations is 100.
That is, the GA will create 100 generations of 6
chromosomes before stopping.
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Chromosome locations on the surface of the
“peak” function: initial population
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Chromosome locations on the surface of the
“peak” function: first generation
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Chromosome locations on the surface of the
“peak” function: local maximum
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Chromosome locations on the surface of the
“peak” function: global maximum
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Performance graphs for 100 generations of 6
chromosomes: local maximum
pc = 0.7, pm = 0.001
0. 7
0. 6
0. 5
Fitness
0. 4
0. 3
0. 2
0. 1
Best
Average
0
-0.1
0
10
20
30
40
50
60
70
80
90
100
Generations
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Performance graphs for 100 generations of 6
chromosomes: global maximum
pc = 0.7, pm = 0.01
1.8
1.6
1.4
Fitness
1.2
1.0
0.8
0.6
Best
Average
0.4
0.2
0
10
20
30
40
50
60
70
80
90
100
Generations
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Performance graphs for 20 generations of
60 chromosomes
pc = 0.7, pm = 0.001
1.8
1.6
1.4
Fitness
1. 2
1.0
0.8
0.6
Best
Average
0.4
0.2
0
2
46
8
10
12
14
16
18
20
Generations
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Case study: maintenance scheduling
 Maintenance scheduling problems are usually
solved using a combination of search techniques
and heuristics.
 These problems are complex and difficult to
solve.
 They are NP-complete and cannot be solved by
combinatorial search techniques.
 Scheduling involves competition for limited
resources, and is complicated by a great number
of badly formalised constraints.
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Steps in the GA development
1. Specify the problem, define constraints and
optimum criteria;
2. Represent the problem domain as a
chromosome;
3. Define a fitness function to evaluate the
chromosome performance;
4. Construct the genetic operators;
5. Run the GA and tune its parameters.
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Case study
Scheduling of 7 units in 4 equal intervals
The problem constraints:
 The maximum loads expected during four intervals are
80, 90, 65 and 70 MW;
 Maintenance of any unit starts at the beginning of an
interval and finishes at the end of the same or adjacent
interval. The maintenance cannot be aborted or finished
earlier than scheduled;
 The net reserve of the power system must be greater or
equal to zero at any interval.
The optimum criterion is the maximum of the net
reserve at any maintenance period.
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Case study
Unit data and maintenance requirements
Unit
number
Unit capacity,
MW
Number of intervals required
for unit maintenance
12
21
33
44
51
61
71
0
5
5
0
5
5
0
2
2
1
1
1
1
1
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Case study
Unit gene pools
Unit 1:
Unit 2:
Unit 3:
Unit 4:
Unit 5:
Unit 6:
Unit 7:
1 1 0 0
0 1
0
0 0 1 1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
Chromosome for the scheduling problem
Unit 1
Unit 2
Unit 3
Unit 4
Unit 5
Unit 6
Unit 7
0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0
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Case study
The crossover operator
Parent 1
0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0
Parent 2
1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0
Child 1
0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0
Child 2
1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0
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Case study
The mutation operator
1 1 0 0 0 1 1 0 0 1
10 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0
1 1 0 0 0 1 1 0 0 0
10 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0
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Performance graphs and the best maintenance
schedules created in a population of 20 chromosomes
N = 20, pc = 0.7, pm = 0.001
Fitness
15
10
5
Best
Average
0
-5
-10
0
5
10
15
20
25
30
35
40
45
50
Generations
N e t r e s e r v e s:
150
15
Unit 4
35
Unit 2
Unit 7
120
MW
Unit 5
35
Unit 2
25
Unit 1
Unit 1
Unit 6
Unit 3
3
4
90
60
30
0
1
2
Timeinterval
(a) 50 generations
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Performance graphs and the best maintenance
schedules created in a population of 20 chromosomes
N = 20, p c = 0.7, pm = 0.001
Fitness
20
Be st
Average
10
0
-10
0
10
30
20
60
50
40
Generations
70
80
90
100
Net rese rves:
150
120
40
25
20
Unit 1
Unit 7
Unit 1
Unit 2
Unit 2
25
Unit 4
Unit 3
Unit 5
Unit 6
90
60
30
0
12
34
Time interval
(b) 100 generations
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Performance graphs and the best maintenance
schedules created in a population of 100 chromosomes
N = 100, p c = 0.7, pm = 0.001
Fitness
30
20
10
Best
Average
0
-10
0
10
20
30
60
40
50
Generations
70
80
90
100
N e t r e s e r v e s:
150
35
Unit 3
25
25
Unit 1
Unit 1
Unit 2
Unit 5
Unit 7
120
90
25
Unit 4
Unit 6
60
30
0
12
34
Time
in terval
(a) Mutation rate is 0.001
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Performance graphs and the best maintenance
schedules created in a population of 100 chromosomes
N = 100,
Fitness
30
c = 0.7,
m = 0.01
20
10
0
Best
Average
-10
-20
0
10
20
30
40
50
60
G e n e r a ti o n s
70
80
90
100
N e t r e s e r v e s:
150
120
25
25
30
Unit 1
Unit 1
Unit 2
Unit 6
Unit 7
Unit 2
1
2
30
Unit 3
Unit 4
Unit 5
90
60
30
0
3
4
T im e i n t e r v a l
(b) Mutation rate is 0.01
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