Mengdi Wu x103197 1 Introduction What are Genetic Algorithms What is Fuzzy Logic Fuzzy Genetic Algorithm 2 What are Genetic Algorithms Software programs that learn in an evolutionary manner similarly to the way biological system evolve ID: 541769
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Fuzzy Genetic Algorithm
Mengdi Wu x103197
1Slide2
Introduction
What are Genetic Algorithms?
What is Fuzzy Logic?
Fuzzy
Genetic Algorithm
2Slide3
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:
Selection
: replicates the most successful solution found in a population
Crossover(Recombination)
: decomposes two distinct solutions and then randomly mixes their parts to form new solutions
Mutation
: randomly changes a candidate solution
4Slide5
Genetic Algorithm Flow Chart
Selection
Mating
Terminate
Initial Population
Crossover
Mutation
The evolution usually starts from a population of randomly generated individuals
Individual solutions are selected through a fitness-based process
This generation process is repeated until a termination condition has been reached
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 techniqueGas 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 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 flexibilityProvides 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
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Flowchart of FGA
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Thank you !
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