lecture 8: 24/5/1435 genetic algorithms lecturer/ kawther abas [email protected] 363cs –...
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Lecture 8: 24/5/1435
Genetic Algorithms
Lecturer/ Kawther [email protected]
363CS – Artificial Intelligence
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Genetic Algorithm
Developed: USA in the 1970’sGenetic Algorithms have been
applied successfully to a variety of AI applications
For example, they have been used to learn collections of rules for robot control.
Genetic Algorithms and genetic programming are called Evolutionary Computation
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Genetic Algorithms (GAs) andGenetic Programming (GP)
Genetic Algorithms◦Optimising parameters for problem solving◦Represent the parameters in the solution(s)
As a “bit” string normally, but often something else
◦Evolve answers in this representationGenetic Programming
◦Representation of solutions is richer in general
◦Solutions can be interpreted as programs◦Evolutionary process is very similar
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GA
Genetic algorithms provide an AI
method by an analogy of biological
evolution
It constructs a population of evolving
solutions to solve the problem
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Genetic AlgorithmsWhat are they?
◦ Evolutionary algorithms that make use of operations like mutation, recombination, and selection
Uses?◦ Difficult search problems◦ Optimization problems◦ Machine learning◦ Adaptive rule-bases
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Classical GAsRepresentation of parameters is a bit string
◦ Solutions to a problem represented in binary◦ 101010010011101010101
Start with a population (fairly large set)◦ Of possible solutions known as individuals
Combine possible solutions by swapping material◦ Choose the “best” solutions to swap material
between and kill off the worse solutions◦ This generates a new set of possible solutions
Requires a notion of “fitness” of the individual◦ Base on an evaluation function with respect to
the problem
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Genetic Algorithm
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Genotype space = {0,1}L
Phenotype space
Encoding (representation)
Decoding(inverse representation)
011101001
010001001
10010010
10010001
Representation
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GA RepresentationGenetic algorithms are
represented as geneEach population consists of a
whole set of genesUsing biological reproduction,
new population is created from old one.
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The Initial PopulationRepresent solutions to problems
◦As a bit string of length LChoose an initial population size
◦Generate length L strings of 1s & 0s randomly
Strings are sometimes called chromosomes◦Letters in the string are called
“genes”◦We call the bit-string “individuals”
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Initialization
Initial population must be a
representative sample of the
search space
Random initialization can be a
good idea (if the sample is large
enough)
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The geneEach gene in the population is
represented by bit strings.
001 10 10Outlook Wind play tennis
0011010
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Gene ExampleThe idea is to use a bit string to
describe the value of attributeThe attribute Outlook has 3
values (sunny, overcast, raining)So we use 3 bit length to
represent attribute outlook010 represent the outlook =
overcast
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GA
The fitness function evaluates
each solution and decide it will be
in next generation of solutions