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Learning Behavior using Genetic Algorithms and Fuzzy Logic GROUP #8 Maryam Mustafa 05020084 Sarah Karim 05020259
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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
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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
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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
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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
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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
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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
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