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Artificial Intelligence
Genetic Algorithms
Source: www.myreaders.info
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Genetic Algorithms• Genetic algorithms are a part of evolutionary computing,
which is a rapidly growing area of artificial intelligence.
• Genetic algorithms are inspired by Darwin's theory about evolution.
• It is an intelligent random search technique used to solve optimization problem
• Although randomized but GA’s exploit historical information to direct the search into the region of better performance within the search space.
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Genetic Algorithms
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Why Genetic Algorithms?
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Optimization
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Optimization
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Search Optimization Algorithms
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Search Optimization Algorithms
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Biological Background – Basic Genetics
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Biological Background – Basic Genetics
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Biological Background – Basic Genetics
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Biological Background – Basic Genetics
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Biological Background – Basic Genetics
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Biological Background – Basic Genetics
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Search Space
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Working Principles
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Working Principles
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Outline of Basic Genetic Algorithm
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Outline of Basic Genetic Algorithm
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Encoding- Genetic Algorithms
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Encoding- Genetic Algorithms
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Binary Encoding
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Binary Encoding
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Value Encoding
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Permutation Encoding
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Permutation Encoding
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Tree Encoding
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Tree Encoding
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Operators of Genetic Algorithm
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Operators of Genetic Algorithm
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Reproduction – or Selection
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Reproduction – or Selection
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Reproduction – or Selection
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Example of Selection
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Roulette Wheel Selection
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Roulette Wheel Selection
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Roulette Wheel Selection
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Boltzmann Selection
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Boltzmann Selection
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Crossover operator
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One-point Crossover
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Two-point Crossover
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Uniform Crossover
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Arithmetic Crossover
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Heuristic Crossover
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Mutation
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Mutation
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Flip Bit
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Boundary
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Non Uniform
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Uniform
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Gaussian
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Examples
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2
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Genetic Algorithm Approach to problem Maximize f(x)=x2