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# Artificial Intelligence

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Artificial Intelligence
Genetic Algorithms Source:

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.

Genetic Algorithms

Why Genetic Algorithms?

Optimization

Optimization

Search Optimization Algorithms

Search Optimization Algorithms

Biological Background – Basic Genetics

Biological Background – Basic Genetics

Biological Background – Basic Genetics

Biological Background – Basic Genetics

Biological Background – Basic Genetics

Biological Background – Basic Genetics

Search Space

Working Principles

Working Principles

Outline of Basic Genetic Algorithm

Outline of Basic Genetic Algorithm

Encoding- Genetic Algorithms

Encoding- Genetic Algorithms

Binary Encoding

Binary Encoding

Value Encoding

Permutation Encoding

Permutation Encoding

Tree Encoding

Tree Encoding

Operators of Genetic Algorithm

Operators of Genetic Algorithm

Reproduction – or Selection

Reproduction – or Selection

Reproduction – or Selection

Example of Selection

Roulette Wheel Selection

Roulette Wheel Selection

Roulette Wheel Selection

Boltzmann Selection

Boltzmann Selection

Crossover operator

One-point Crossover

Two-point Crossover

Uniform Crossover

Arithmetic Crossover

Heuristic Crossover

Mutation

Mutation

Flip Bit

Boundary

Non Uniform

Uniform

Gaussian

Examples

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

Genetic Algorithm Approach to problem Maximize f(x)=x2

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