Fuzzy Genetic Algorithm Mengdi Wu x103197 Fuzzy Genetic Algorithm

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

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.

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

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

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

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

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.

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

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

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.

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

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

Flowchart of FGA

Thank you !