/
Fuzzy Genetic Algorithm
Fuzzy Genetic Algorithm

Fuzzy Genetic Algorithm - PowerPoint Presentation

phoebe-click
phoebe-click . @phoebe-click
Follow
249 views | Public

Fuzzy Genetic Algorithm - Description

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 Download Presentation

Tags :

genetic fuzzy fga algorithm fuzzy genetic algorithm fga defined solutions solution logic process selection population criteria fitness search randomly

Share:

Link:

Embed:

Please download the presentation from below link :


Download Presentation - The PPT/PDF document "Fuzzy Genetic Algorithm" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Presentation on theme: "Fuzzy Genetic Algorithm"— Presentation transcript

Slide1

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.

3Slide4

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

5Slide6

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

6Slide7

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

7Slide8

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.

8Slide9

Advantages of Fuzzy Logic

Provides flexibilityProvides options

Allow for observation

Increases the system’s maintainability

Control situation not easily defined by mathematical solutions

9Slide10

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

10Slide11

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.

11Slide12

FGA Model

The algorithm has two computational elements that work together

The Genetic Algorithm(GA)

The Fuzzy Fitness Finder(FFF)

12Slide13

Steps of Fuzzy in FGA

13Slide14

Flowchart of FGA

14Slide15

Thank you !

15