Thomas Jansen
ABSTRACT
Evolutionary algorithms and other nature-inspired search heuristics like ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. In recent years, analyses has shown that these general randomized search heuristics can be analyzed like "ordinary" randomized algorithms and that such analyses of the expected optimization time yield deeper insights in the functioning of evolutionary algorithms in the context of approximation and optimization. This is an important research area where a lot of interesting questions are still open.
The tutorial enables attendees to analyze the computational complexity of evolutionary algorithms and other search heuristics in a rigorous way. An overview of the tools and methods developed within the last 15 years is given and practical examples of the application of these analytical methods are presented.
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Digital Library
- B. Doerr, D. Johannsen (2007): Adjacency list matchings - an ideal genotype for cycle covers. GECCO. To appear. Google ScholarDigital Library
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- D. Sudholt (2005): Crossover is provably essential for the Ising Model on Trees. In GECCO, 1161-1167. Google ScholarDigital Library
- T. Jansen, D. Weyland (2007): Analysis of evolutionary algorithms for the longest common subsequence problem. In GECCO. To appear. Google ScholarDigital Library
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- C. Witt (2005): Worst-case and average-case approximations by simple randomized search heuristics. In 22nd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 44-56. Google ScholarDigital Library
- T. Jansen, R. P. Wiegand (2004): The cooperative coevolutionary (1+1) EA. Evolutionary Computation 12(4):405-434. Google ScholarDigital Library
- D. Sudholt (2006): Local search in evolutionary algorithms: the impact of the local search frequency. In 17th International Symposium on Algorithms and Computation (ISAAC), 359-368. Google ScholarDigital Library
- R. Watson, T. Jansen (2007): A building-block royal road where crossover is provably essential. In GECCO. To appear. Google ScholarDigital Library
- B. Doerr, F. Neumann, D. Sudholt, C. Witt (2007): On the Runtime Analysis of the 1-ANT ACO Algorithm. GECCO. To appear. Google ScholarDigital Library
- S. Droste (2005): Not all linear functions are equally difficult for the compact genetic algorithm. In GECCO, 679-686. Google ScholarDigital Library
Index Terms
- Computational complexity and evolutionary computation
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