Coloring random and semi-random k-colorable graphs
Journal of Algorithms
Randomized algorithms
Algorithmic theory of random graphs
Random Structures & Algorithms - Special issue: average-case analysis of algorithms
Finding a large hidden clique in a random graph
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Finding and certifying a large hidden clique in a semirandom graph
Random Structures & Algorithms
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Heuristics for semirandom graph problems
Journal of Computer and System Sciences
Approximating Maximum Clique by Removing Subgraphs
SIAM Journal on Discrete Mathematics
How randomized search heuristics find maximum cliques in planar graphs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Better inapproximability results for maxclique, chromatic number and min-3lin-deletion
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
On the choice of the parent population size*
Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Finding mount everest and handling voids
Evolutionary Computation
| Hi-index | 5.23 |
Surprisingly, general heuristics often solve some instances of hard combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple randomized optimizers on the maximum clique problem is investigated. We focus on semi-random models for sparse graphs, in which an adversary is even allowed to insert a limited number of edges (and not only to remove them). In the course of these investigations the approximation behavior on general graphs and the optimization behavior for sparse graphs and further semi-random graph models are also considered. With regard to the optimizers, particular interest is given to the influences of the population size and the search operator.