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FUZZY GENETIC ALGORITHMMengdi Wu x103197

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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

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 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

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 !