1. Fuzzy Genetic Algorithm
Mengdi Wu x103197
Fuzzy Genetic Algorithm

2. Introduction What are Genetic Algorithms? What is Fuzzy Logic?
Fuzzy Genetic Algorithm

3. 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.

4. 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

5. Genetic Algorithm Flow Chart
Initial Population
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
Selection
Mating
Crossover
Mutation
Terminate

6. 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

7. 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

8. What is Fuzzy Logic? Definition of Fuzzy Definition of Fuzzy Logic
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.

9. Advantages of Fuzzy Logic
Provides flexibility
Provides options
Allow for observation
Increases the system’s maintainability
Control situation not easily defined by mathematical solutions

10. 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

11. 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.

12. FGA Model
The algorithm has two computational elements that work together
The Genetic Algorithm(GA)
The Fuzzy Fitness Finder(FFF)

13. Steps of Fuzzy in FGA The Fuzzy Fitness Finder
Input and Output Criteria
Fuzzification of Inputs
Fuzzy Inference Engine
Defuzzification of Output

14. Flowchart of FGA

15. Thank you !