1. Genetic Algorithms overview
Date : May 30, 2009
Presenter : Hyun-Tae Kim

2. Contents Self introduction The Genetic Algorithms Prologue Principle
Fundamentals of Genetic Algorithms
Examples
Q & A

3. Self Introduction

4. Who am I ? Name : Hyun-Tae Kim Birth : Aug 28th . 1983 (27 years)
1st semester on Master course.
I’ve enrolled in SEAL from this semester.
SKK Evolutionary Algorithm Lab
Advising Prof. Chang Wook Ahn

5. I want to … Study Genetic Programming Information Retrieval System
Web x.0
Be a expert on my field and also be warm-hearted person.

6. The Genetic Algorithms

7. Deterministic Approach
Prologue
Deterministic VS Heuristic Approach
Deterministic Approach
Heuristic Approach
Simplex
Method
Linear
Programming
Gradient
Decent
Genetic
Algorithm
Particle
Swarm Opt.
Ant
Colony Opt.
Random
Search
Nature-Inspired
Methods
Bayesian
Inference

8. Principle What is the Genetic Algorithm(G.A) ?
G.A is a search technique used in computing to find exact or approximate solutions to optimization and search problems.
G.A is categorized as global search heuristics.
G.A is a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

9. Implications for applying
Principle cont’
Lessons from Biological Evolution
Implications for applying
to computing techs.
Multiple
Surviving
Mixing
Generation
Population
Mating pool
Mates Selected
Mating
Off-Spring
New Population

10. Fundamentals of G.A Basic Terminology in G.A
Individual - Any possible solution
Population - Group of all individuals
Search Space - All possible solutions to the problem
Chromosome - Blueprint for an individual

11. Fundamentals of G.A cont’
Basic Elements in G.A
Encoding Scheme
Fitness Function
Genetic Operators
Parameter Setting

12. Fundamentals of G.A cont’
Basic Elements in G.A - Encoding Scheme
Encode possible solution to chromosome.
Binary Encoding, n-ary Encoding.
Search
space 1
population A B C D

13. Fundamentals of G.A cont’
Basic Elements in G.A – Fitness Function
A particular type of objective function. It quantifies the optimality of a solution A particular chromosome may be ranked against all the other chromosomes
Fitness
evaluation
Roulette wheel selection
Raking Selection
Tournament Selection

14. Fundamentals of G.A cont’
Basic Elements in G.A – Genetic Operator
Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (recombination or crossover).

15. Fundamentals of G.A cont’
Basic Elements in G.A – Genetic Operator
Crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based.

16. Fundamentals of G.A cont’
Basic Elements in G.A – Genetic Operator
Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of chromosomes to the next. It is analogous to biological mutation.

17. Fundamentals of G.A cont’
Basic Elements in G.A – Parameter Setting
many parameters which use in G.A
Population size
Proportion of Crossover / Mutation etc.

18. Fundamentals of G.A cont’
How does genetic algorithm work ?
A population is created with a group of individuals created randomly.
The individuals in the population are then evaluated. The evaluation function(=fitness function) is provided by the programmer and gives the individuals a score based on how well they perform at the given task.
Two individuals are then selected based on their fitness, the higher the fitness, the higher the chance of being selected. These individuals then "reproduce(=crossover)" to create one or more offspring, after which the offspring are mutated randomly.
This continues until a suitable solution has been found or a certain number of generations have passed, depending on the needs of the programmer.

19. Fundamentals of G.A cont’
Procedure of G.A
Search
space 1
mutation
cross over
reproduction 1
selection 1
population A B C D
Fitness
evaluation
Substitution
(New Population)

20. Examples
Mona Lisa
Traveling Salesman Problem on Sony PSP

21. Q & A
Any Question ?

22. Thank You for your attention