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CS440 Computer Science Seminar

CS440 Computer Science Seminar. Introduction to Evolutionary Computing. Adaptation to environment. Traveling salesman problem. A salesperson must visit clients in different cities, and then return home. What is the shortest tour through those cities, visiting each one once and only once?

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CS440 Computer Science Seminar

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  1. CS440 Computer Science Seminar Introduction to Evolutionary Computing

  2. Adaptation to environment

  3. Traveling salesman problem • A salesperson must visit clients in different cities, and then return home. What is the shortest tour through those cities, visiting each one once and only once? • No known algorithms are able to generate the best answer in an amount time that grow only as a polynomial function of the number of elements (cities) in the problem. • Belongs in the NP-hard class of problems, where NP stands for non-deterministic polynomial. For 100 cities, there are over 10155 different possible paths through all cities. The Universe is only 1018 seconds old!

  4. Evolution Algorithm

  5. Steps of evolutionary approach to discovering solutions • Choosing the solution representation • Devising a random variation operator • Determining a rule for solution survival • Initialization the population

  6. Solving the traveling salesman problem: 1. Solution representation, 2. Devising random variation operator

  7. Solving the traveling salesman problem: 3. Determining the rule for solution survival, 4. Initialize the population • Rule for survival: survival of the fittest—the least total distance traveled. • Initial population: in this case, chosen completely at random from the space of possible solutions.

  8. The best result of the 1st generation for the 100-city traveling salesman problem

  9. The best result of the 500th generation for the 100-city traveling salesman problem

  10. The best result of the 1000th generation for the 100-city traveling salesman problem

  11. The best result of the 4000th generation for the 100-city traveling salesman problem

  12. Drug design using evolutionary algorithm

  13. Evolutionary algorithm in high-level chess game

  14. To probe further • What is revolutionary computation, IEEE Spectrum, Feb. 2000 • How to solve It: Modern Heuristics, Zbigniew Michalewicz, Springer, 2000 • Evolution, Neural Networks, Games, and Intelligence, Kumar and Fogel, Proceedings of IEEE Vol. 87, no 9, pp. 1471-96, Sept. 1999

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