The electrical resistance of a graph captures its commute and cover times
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Heuristics for semirandom graph problems
Journal of Computer and System Sciences
On the Analysis of Dynamic Restart Strategies for Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Journal of Combinatorial Theory Series B
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Analysis of evolutionary algorithms for the longest common subsequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Finding large cliques in sparse semi-random graphs by simple randomized search heuristics
Theoretical Computer Science
Computational complexity and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computational complexity and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
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Surprisingly, general search heuristics often solve combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple randomized optimizers on the maximum clique problem on planar graphs is investigated rigorously. The focus is on the worst-, average-, and semi-average-case behaviors. In semi-random planar graph models an adversary is allowed to modify moderately a random planar graph, where a graph is chosen uniformly at random among all planar graphs. With regard to the heuristics particular interest is given to the influences of the following four popular strategies to overcome local optima: local- vs. global-search, single- vs. multi-start, small vs. large population, and elitism vs. non-elitism selection. Finally, the black-box complexities of the planar graph models are analyzed.