Fundamentals of Genetic Algorithms Artificial

RC Chakraborty, www.myreaders.info Fundamentals of Genetic Algorithms : AI Course Lecture 39 – 40, notes, slides www.myreaders.info/ , RC Chakraborty, e-mail rcchak@gmail.com , June 01, 2010 www.myreaders.info/html/artificial_intelligence.html Fundamentals of Genetic Algorithms Artificial Intelligence www.myreaders.info Return to Website Genetic algorithms, topics : Introduction, search optimization algorithm; Evolutionary algorithm (EAs); Genetic Algorithms (GAs) : biological background, search space, working principles, basic genetic algorithm, flow chart for Genetic programming; Encoding : binary encoding, value encoding, permutation encoding, and tree encoding; Operators of genetic

algorithm : reproduction or selection - roulette wheel selection, Boltzmann selection; fitness function; Crossover – one point crossover, two Point crossover, uniform crossover, arithmetic, heuristic; Mutation - flip bit, boundary, non- uniform, uniform, Gaussian; Basic genetic algorithm - solved examples : maximize function f(x) = x 2 and two bar pendulum. RC Chakraborty, www.myreaders.info Fundamentals of Genetic Algorithms Artificial Intelligence Topics (Lectures 39, 40 2 hours) Slides 1. Introduction Why genetic algorithms, Optimization, Search optimization algorithm; Evolutionary algorithm (EAs); Genetic Algorithms (GAs) : Biological background, Search space, Working principles, Basic genetic algorithm, Flow chart for Genetic programming. 03-15 2. Encoding Binary Encoding, Value Encoding, Permutation Encoding, and Tree Encoding. 16-21 3. Operators of Genetic Algorithm Reproduction or selection : Roulette wheel selection, Boltzmann selection; fitness function; Crossover: one-Point crossover, two-Point crossover, uniform crossover, arithmetic, heuristic; Mutation : flip bit, boundary, non-uniform, uniform, Gaussian. 22-35 4. Basic Genetic Algorithm Solved examples : maximize function f(x) = x 2 and two bar pendulum. 36-41 5. References 42 02 RC Chakraborty, www.myreaders.info Fundamentals of Genetic Algorithms What are GAs ? • Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. • Genetic algorithms (GAs) are a part of Evolutionary computing, a rapidly growing area of artificial intelligence. GAs are inspired by Darwin's theory about evolution - "survival of the fittest". • GAs represent an intelligent exploitation of a random search used to solve optimization problems. • GAs, although randomized, exploit historical information to direct the search into the region of better performance within the search space. • In nature, competition among individuals for scanty resources results in the fittest individuals dominating over the weaker ones. 03 RC Chakraborty, www.myreaders.info GA - Introduction 1. Introduction to Genetic Algorithms Solving problems mean looking for solutions, which is best among others. Finding the solution to a problem is often thought : − In computer science and AI, as a process of search through the space of possible solutions. The set of possible solutions defines the search space (also called state space) for a given problem. Solutions or partial solutions are viewed as points in the search space. − In engineering and mathematics, as a process of optimization. The problems are first formulated as mathematical models expressed in terms of functions and then to find a solution, discover the parameters that optimize the model or the function components...

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