Genetic Algorithm

GENETIC ALGORITHMS
Rohith Nandakumar
S7 CS
06348

INTRODUCTION
Genes are the basic “instructions” for building an organism
Gene represents a specific trait of
the organism. Ex: Eye color
A gene may have different settings
A chromosome is a sequence of genes

EVOLUTION
Organisms (animals or plants) produce a number of offspring which are almost, but not entirely, like themselves
Variation may be due to mutation (random changes)
Variation may be due to combination of some characteristics from each parent
Some of these offspring may survive to produce offspring of their own - some won’t
The “better adapted” offspring are more likely to survive
Over time, later generations become better and better adapted
Genetic algorithms use this same process to “evolve” better programs

GENETIC ALGORITHMS
Inspired by Darwin's theory about evolution
Theyare a part of evolutionary computing, which is a rapidly growing area of artificial intelligence
Idea of evolutionary computing was introduced in the 1960s by I.Rechenberg
Genetic Algorithms (GAs) were invented by John Holland
In 1992 John Kozahas used genetic algorithm to evolve programs to perform certain tasks. He called his method "genetic programming" (GP)
LISP programs were used, because programs in this language can expressed in the form of a "parse tree", which is the object the GA works on

THE BASIC ALGORITHM
Suppose your “organisms” are 32-bit computer words
You want a string in which all the bits are ones
Here’s how you can do it:
Create 100 randomly generated computer words
Repeatedly do the following:
Count the 1 bits in each word
Exit if any of the words have all 32 bits set to 1
Keep the ten words that have the most 1s (discard the rest)
From each word, generate 9 new words as follows:
Choose one of the other words
Take the first half of this word and combine it with the second half of the other word
Pick a random bit in the word and toggle (change) it
Note that this procedure does not guarantee that the next “generation” will have more 1 bits, but it’s likely

ENCODING
The chromosome should in some way contain information about solution which it represents
The most used way of encoding is a binary string. The chromosome then could look like this:
Each chromosome has one binary string
Each bit in this string can represent some characteristic of the solution. Or the whole string can represent a number.

CROSSOVER
Crossover selects genes from parent chromosomes and creates a new offspring
A crossover point is chosen at random
Everything before this point, copy from a first parent and then everything after it copy from the second parent.
There are other ways how to make crossover
Specific crossover made for a specific problem can improve performance of the genetic algorithm

MUTATION
Mutation prevents the algorithm to be trapped in a local minima
Changes are made in the offspring randomly
The mutation depends on the encoding as well as the crossover.

SELECTION
Chromosomes are selected from the population to be parents to crossover
Main idea: better individuals get higher chance to reproduce
Chances proportional to fitness
Implementation: Roulette Wheel technique
Assign to each individual a part of the roulette wheel
Spin the wheel n times to select n individuals

AN EXAMPLE:TRAVELLING SALESMAN PROBLEM

INITIAL POPULATION FOR TSP
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(4,3,6,2,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)

SELECT PARENTS
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(4,3,6,2,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
Try to pick the better ones.

CREATE OFFSPRING
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(4,3,6,2,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
(3,4,5,6,2)

CREATE MORE OFFSPRING
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(4,3,6,2,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)

MUTATE
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(4,3,6,2,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)

MUTATE
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(2,3,6,4,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)

ELIMINATE
(5,3,4,6,2)
(2,4,6,3,5)
(4,3,6,5,2)
(2,3,4,6,5)
(2,3,6,4,5)
(3,4,5,2,6)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)
(3,4,5,6,2)
(5,4,2,6,3)
Tend to kill off the worst ones.

INTEGRATE
(5,4,2,6,3)
(5,3,4,6,2)
(2,4,6,3,5)
(2,3,6,4,5)
(3,4,5,2,6)
(3,4,5,6,2)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)

RESTART
(5,3,4,6,2)
(2,4,6,3,5)
(5,4,2,6,3)
(2,3,6,4,5)
(3,4,5,2,6)
(3,4,5,6,2)
(3,5,4,6,2)
(4,5,3,6,2)
(5,4,2,3,6)
(4,6,3,2,5)
(3,4,2,6,5)
(3,6,5,1,4)

GENETIC PROGRAMMING
A string of bits could represent a program
If you want a program to do something, you might try to evolve one
As a concrete example, suppose you want a program to help you choose stocks in the stock market
There is a huge amount of data, going back many years
What data has the most predictive value?
What’s the best way to combine this data?
A genetic program is possible in theory, but it might take years to evolve into something useful

APPLICATIONS OF GA
Travelling Salesman Problem
Artificial Life (A-Life)
Robotics
Automotive Design
Evolvable Hardware
Computer Gaming
Optimizing Chemical Kinetic Analysis
Encryption and Code Breaking

CONCLUSION
Genetic algorithms are -
Fun! They are enjoyable to program and to work with
Mind-bogglingly slow - you don’t want to use them if you have any alternatives
Good for a very few types of problems
Genetic algorithms can sometimes come up with a solution when you can see no other way of tackling the problem

Q & A
?

THANKYOU…

http://www.webicina.com	16
http://www.slideshare.net	12
http://www.lmodules.com	1

Genetic Algorithm

by rabidityfactor on Oct 17, 2009

Accessibility

Categories

Tags

Upload Details

Usage Rights

3 Embeds 29

Statistics

Genetic Algorithm — Presentation Transcript