Genetic Algorithms

Topics: Genetic algorithm, Genetic algorithms, Evolution Pages: 118 (7929 words) Published: December 27, 2012
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www.myreaders.info/ , RC Chakraborty, e-mail rcchak@gmail.com , June 01, 2010 www.myreaders.info/html/artificial_intelligence.html

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Fundamentals of Genetic Algorithms : AI Course Lecture 39 – 40, notes, slides

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www.myreaders.info

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Fundamentals of Genetic Algorithms
Artificial Intelligence
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, nonuniform, uniform, Gaussian;

Basic genetic algorithm -

solved

examples : maximize function f(x) = x2 and two bar pendulum.

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Fundamentals of Genetic Algorithms

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Artificial Intelligence

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Topics

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(Lectures 39, 40

2 hours)

1. Introduction

Slides
03-15

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.

2. Encoding

16-21

Binary Encoding, Value Encoding, Permutation Encoding, and Tree Encoding.
3. Operators of Genetic Algorithm

22-35

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.

4. Basic Genetic Algorithm

36-41

Solved examples : maximize function f(x) = x2 and two bar pendulum. 5. References
02

42

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What are GAs ?

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Fundamentals of Genetic Algorithms

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• 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.
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GA - Introduction

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1. Introduction to Genetic Algorithms
Solving

problems

mean

looking

for

solutions,

which

is

best

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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 that provide optimal system performance.

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