Transcript Fuzzy Genetic Algorithm

Mengdi Wu x103197
FUZZY GENETIC ALGORITHM
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Introduction
 What are Genetic Algorithms?
 What is Fuzzy Logic?
 Fuzzy Genetic Algorithm
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What are Genetic Algorithms?
 Software programs that learn in an
evolutionary manner, similarly to the way
biological system evolve.
 Simply, it is a search method that follows a
process that simulates evolution in a
computer.
 “Survival of the Fittest” solution, it works on
large population of solutions that are
repeatedly subjected to selection pressure.
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Genetic Operators
 Three major operations of genetic algorithm
are:
1. Selection: replicates the most successful
solution found in a population
2. Crossover(Recombination): decomposes
two distinct solutions and then randomly
mixes their parts to form new solutions
3. Mutation: randomly changes a candidate
solution
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Genetic Algorithm Flow Chart
Initial Population
• The evolution usually starts
from a population of randomly
generated individuals
Selection
Mating
Crossover
• Individual solutions are
selected through a fitnessbased process
• This generation process is
repeated until a termination
condition has been reached
Mutation
Terminate
• Improve the solution through
repetitive application of the
mutation, crossover, inversion
and selection operators
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Advantage of Genetic Algorithms
 A fast search technique
 Gas will produce “close” to optimal results in
a “reasonable” amount of time
 Suitable for parallel processing
 Fairly simple to develop
 Make no assumptions about the problem
space
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GA is used in
 Dynamic Process Control
 Simulation of models of behavior and
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evolution
Complex design of engineering structres
Pattern Recognition
Scheduling
Transportation and Routing
Layout and Circuit design
Telecommunications
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What is Fuzzy Logic?
 Definition of Fuzzy
 Fuzzy-”not clear, distinct, or precise; blurred”
 Definition of Fuzzy Logic
 A form of knowledge representation suitable for
notions that cannot be defined precisely, but
which depend upon their contexts.
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Advantages of Fuzzy Logic
 Provides flexibility
 Provides options
 Allow for observation
 Increases the system’s maintainability
 Control situation not easily defined by
mathematical solutions
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Fuzzy Genetic Algorithm
 An FGA maybe defined as an ordering
sequence of instructions in which some of the
instructions or algorithm components may be
designed with fuzzy logic based tools
 A fuzzy fitness finding mechanism guides the
GA through the search space by combining
the contributions of various criteria/features
that have been identified as the governing
factors for the formation of the clusters
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Why FGA?
 For any problem solving using GA, it will
involve multiple criteria. In multi-criteria
optimization, the notion of optimality is not
clearly defined.
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FGA Model
The algorithm has two computational elements that
work together
• The Genetic Algorithm(GA)
• The Fuzzy Fitness Finder(FFF)
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Steps of Fuzzy in FGA
• The Fuzzy Fitness Finder
• Input and Output Criteria
• Fuzzification of Inputs
• Fuzzy Inference Engine
• Defuzzification of Output
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Flowchart of FGA
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Thank you !
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