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Artificial Intelligence in Information Processing
Genetic Algorithms
by Theresa Kriesefor Distributed Data Processing
Content• Introduction
• Understanding: Travelling Salesman Problem
• Biological Background
• GA’s in Information Processing
• Summary
• Sources
How to solve problems, that are socomplex, that you can not get an exact
solution in an appropriate time?
Travelling Salesman Problem
The Problem: A Salesman needs to go to n citiesfor work. In each city, he has one customer.Because he doesn’t want to travel so long, heneeds to find the shortest possible route. He knowsthe single distances between two cities.
optimisation problem
not one solution, but the best possible no wrong or right solution
How could you solve the problem?
• for all possible ways, the distance must be found
• with increasing n, the problem soon gets too complex
NP-Algorithm: problem can’t be solved in polynomial time / the needed calculating steps can’t be described by a polynomial
that’s more, than the amount of elementary particles in the universe!
* 10 cities = over 180000 possibilities* 24 cities = 1.3*10^22 poss.* 120 cities = 6*101^96 poss.
This can’t be the right way..
no combinatorial solving
• For practical use:Instead of an optimum
(shortest route ever) after a long time
It’s better to get a suboptimum (short, but probably not the shortest) in the short-run
Example: optimal route for visiting the 15biggest cities in Germany
Let’s ask the
nature! She is
solving complex
problems for
hundreds of
centuries!
Biological Background
Different processes
during the reproduction
of a population in a long
period of time aspire a
perfectly adapted group
of individuals in the end.
Image: http://softwarecreation.org/images/2008/natural-selection.png
Mutation
Images: http://neatorama.cachefly.net/images/2006-07/albino-squirrel-white.jpg, http://employees.csbsju.edu/HJAKUBOWSKI/classes/SrSemMedEthics/Human%20Genome%20Project/mutation2.gif
Selection
Image: http://www.scienceteacherprogram.org/biology/NaturalSelectionIllustration.gif
Recombination (Crossover)
Image: http://en.wikipedia.org/wiki/Image:Morgan_crossover_1.jpg
But how can we use it in Information Processing?
When do we need genetic algorithms?
• Timetabling problems
• Bioinformatics
• Code-breaking
• Software Engineering
• Scheduling applications
• Marketing analysis
• File allocation for distribution systems
• Learning algorithms in neural networks
How does it work?
-Different solution candidates
- fitness function
-Selection
-Mutation
-Recombination
-if break-up criteria is fulfilled
Best found solution
Steps in practice
• Initialisation
- generation of all possible “individuals” (solution candidates) by chance 1st generation
- encoding to binary code
• Evaluation
- using a fitness function, the fitness of each solution candidate is calculated
Process • Selection
- random selection of solution candidates
- the higher the fitness, the higher the probability to be selected
• Mutation
- random modification of candidates
• Recombination (crossing over)
Mutation and crossing over are methods to generate a 2nd generation population.
New generation replaces worst ranked parts of the generation before.
Due to the repeating processes, the generations are getting closer to an optimum.
The whole process continues until a break-up criteria occurs.
Example
Images: http://fbim.fh-regensburg.de/~saj39122/vhb/NN-Script/script/gen/k040401.html
Understanding:Scheduling
• Example: hospital
• working in shifts
• many factors to consider: - law regulations - personal wishes for days off - shift premium - certain amount of doctors and nurses
• very complex information cluster in one big database
• program works out optimum schedule by using genetic algorithms
Summary
• Based on the biological evolutionGenetic operators used: - selection - mutation - recombination
• Developed to solve optimisation problems
• Can not give an exact solution but is approaching an optimum
Sources
• www.wikipedia.org [en,de]
• www-e.uni-magdeburg.de/harbich/genetische_algorithmen [de]
• www.htw-dresden.de/~iwe/Belege/Boerner/ [de]
• http://www.uni-kl.de/AG-AvenhausMadlener/tsp-ger.html [de]
• http://www.sciencedirect.com Volume 39, Issue 5, September 2003, Pages 669-687 [en]
• www.fbim.fh-regensburg.de [de]
THANK YOU FOR ATTENTION!