Learning Behavior using Genetic Algorithms and Fuzzy Logic GROUP #8 Maryam Mustafa Sarah Karim

Introduction  For a given System, certain parameters and their relationships will be defined using Fuzzy Logic  Genetic algorithms will use these pre-fed rules to generate all combinations of rules for the given system

Theory  The set of all the rules will constitute the population of the system  Based on a fitness function,those rules which confirm most to it will be selected as the parents  Through crossover and mutation new rules will be generated

Theory Continued  From the new population the best rules will be selected as parents for the next population  This process will continue until the desired complete rule set for the given system is obtained

Example  Suppose a Robot has to navigate through a room to reach a target  The room has turns and obstacles which the robot has to sense and maneuver around  The parameters selected are Distance from the Wall and speed of the robot

Example Continued  These parameters are represented through Linguistic variables  The system knows that if Distance and Speed high, then turn right and that if Distance and Speed Medium, move back

Example Continued  Through the Fitness Function on of which may be that The distance to the goal is minimum, certain rules will be selected  Crossing over and Mutation will produce new rules such as If Distance High and Speed High, Move Back