artificial intelligence in information processing
DESCRIPTION
Artificial Intelligence in Information Processing. Genetic Algorithms. by Theresa Kriese for Distributed Data Processing. Content. Introduction Understanding: Travelling Salesman Problem Biological Background GA’s in Information Processing Summary Sources. - PowerPoint PPT PresentationTRANSCRIPT
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Artificial Intelligence in Information Processing
Genetic Algorithms
by Theresa Kriesefor Distributed Data Processing
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Content• Introduction
• Understanding: Travelling Salesman Problem
• Biological Background
• GA’s in Information Processing
• Summary
• Sources
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How to solve problems, that are socomplex, that you can not get an exact
solution in an appropriate time?
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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
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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.
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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
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Let’s ask thenature! She is
solving complexproblems forhundreds ofcenturies!
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Biological Background
Different processesduring the reproductionof a population in a longperiod of time aspire aperfectly adapted groupof individuals in the end.
Image: http://softwarecreation.org/images/2008/natural-selection.png
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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
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Selection
Image: http://www.scienceteacherprogram.org/biology/NaturalSelectionIllustration.gif
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Recombination (Crossover)
Image: http://en.wikipedia.org/wiki/Image:Morgan_crossover_1.jpg
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But how can we use it in Information Processing?
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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
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How does it work?
-Different solution candidates
- fitness function
-Selection
-Mutation
-Recombination
-if break-up criteria is fulfilled
Best found solution
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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
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Process • Selection - random selection of solution candidates - the higher the fitness, the higher the probability
to be selected • Mutation - random modification of candidates
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• 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.
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Example
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Images: http://fbim.fh-regensburg.de/~saj39122/vhb/NN-Script/script/gen/k040401.html
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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
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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
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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]
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THANK YOU FOR ATTENTION!