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Artificial Intelligence in Information Processing

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tevy
26 diapositives · 394 Views · Aug 08, 2014

This work explores the application of Genetic Algorithms (GAs) in tackling complex optimization problems, such as the Travelling Salesman Problem (TSP). Traditional methods fail due to exponential complexity as the number of cities increases, making exact solutions impractical. GAs draw inspiration from biological evolution, utilizing mechanisms like selection, mutation, and recombination to evolve solutions. This paper discusses the implementation of GAs in various fields, including timetabling and bioinformatics, demonstrating their effectiveness in producing near-optimal solutions quickly.

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Artificial Intelligence in Information Processing
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  1. Artificial Intelligence in Information Processing Genetic Algorithms by Theresa Kriese for Distributed Data Processing

  2. Content • Introduction • Understanding: Travelling Salesman Problem • Biological Background • GA’s in Information Processing • Summary • Sources

  3. How to solve problems, that are socomplex, that you can not get an exactsolution in an appropriate time?

  4. Travelling Salesman Problem optimisation problem  not one solution, but the best possible  no wrong or right solution The Problem: A Salesman needs to go to n cities for work. In each city, he has one customer. Because he doesn’t want to travel so long, he needs to find the shortest possible route. He knows the single distances between two cities.

  5. 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.

  6. 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 15 biggest cities in Germany

  7. Let’s ask the nature! She is solving complex problems for hundreds of centuries!

  8. 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

  9. 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

  10. Selection Image: http://www.scienceteacherprogram.org/biology/NaturalSelectionIllustration.gif

  11. Recombination (Crossover) Image: http://en.wikipedia.org/wiki/Image:Morgan_crossover_1.jpg

  12. But how can we use it in Information Processing?

  13. 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

  14. How does it work? • Different solution candidates • fitness function • Selection • Mutation • Recombination -if break-up criteria is fulfilled  Best found solution

  15. 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

  16. Process • Selection - random selection of solution candidates - the higher the fitness, the higher the probability to be selected • Mutation - random modification of candidates

  17. 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.

  18. Example

  19. Images: http://fbim.fh-regensburg.de/~saj39122/vhb/NN-Script/script/gen/k040401.html

  20. 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

  21. Summary • Based on the biological evolution • Genetic operators used: - selection - mutation - recombination • Developed to solve optimisation problems • Can not give an exact solution but is approaching an optimum

  22. 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]

  23. THANK YOU FOR ATTENTION!