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

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

Self Introduction

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

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

The Genetic Algorithms

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

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).

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

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

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

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

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

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).

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.

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.

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.

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.

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)

Examples Mona Lisa Traveling Salesman Problem on Sony PSP

Q & A Any Question ? http://arkii.tistory.com

Thank You for your attention