Genetic Algorithms & Modeling
Soft Computing
Genetic algorithms & Modeling, topics : Introduction, why genetic
algorithm? search optimization methods, evolutionary algorithms
(EAs), genetic algorithms (GAs) - biological background, working
principles; basic genetic algorithm, flow chart for Genetic
Programming. Encoding : binary encoding, value encoding,
permutation encoding, tree encoding. Operators of genetic
algorithm : random population, 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,
Gaussian, non-uniform, and uniform; Basic genetic algorithm :
examples - maximize function f(x) = x2 and two bar pendulum.
, Genetic Algorithms & Modeling
Soft Computing
Topics
(Lectures 37, 38, 39, 40 4 hours)
Slides
1. Introduction 03-23
What are Genetic Algorithms and why Genetic
Search
Algorithm? optimization methods; Evolutionary
Genetic
Algorithms (EAs), Basic
Algorithms (GAs) : Biological background, Working principles,
Genetic Algorithm, Flow chart for Genetic Programming.
2. Encoding 24-29
Binary Encoding, Value Encoding, Permutation Encoding, Tree
Encoding.
3. Operators of Genetic Algorithm 30-43
Random population, 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, Gaussian, Non-uniform, and
Uniform;
4. Basic Genetic Algorithm 44-49
Examples : Maximize function f(x) = x2 and Two bar pendulum.
5. References 50
02
, Genetic Algorithms & Modeling
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
, SC – GA - Introduction
1.Introduction
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 that provide optimal
system performance.
04
Soft Computing
Genetic algorithms & Modeling, topics : Introduction, why genetic
algorithm? search optimization methods, evolutionary algorithms
(EAs), genetic algorithms (GAs) - biological background, working
principles; basic genetic algorithm, flow chart for Genetic
Programming. Encoding : binary encoding, value encoding,
permutation encoding, tree encoding. Operators of genetic
algorithm : random population, 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,
Gaussian, non-uniform, and uniform; Basic genetic algorithm :
examples - maximize function f(x) = x2 and two bar pendulum.
, Genetic Algorithms & Modeling
Soft Computing
Topics
(Lectures 37, 38, 39, 40 4 hours)
Slides
1. Introduction 03-23
What are Genetic Algorithms and why Genetic
Search
Algorithm? optimization methods; Evolutionary
Genetic
Algorithms (EAs), Basic
Algorithms (GAs) : Biological background, Working principles,
Genetic Algorithm, Flow chart for Genetic Programming.
2. Encoding 24-29
Binary Encoding, Value Encoding, Permutation Encoding, Tree
Encoding.
3. Operators of Genetic Algorithm 30-43
Random population, 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, Gaussian, Non-uniform, and
Uniform;
4. Basic Genetic Algorithm 44-49
Examples : Maximize function f(x) = x2 and Two bar pendulum.
5. References 50
02
, Genetic Algorithms & Modeling
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
, SC – GA - Introduction
1.Introduction
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 that provide optimal
system performance.
04
