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| Content Provider | ACM Digital Library |
|---|---|
| Author | Neumann, Frank Kratsch, Stefan |
| Abstract | In this paper, we consider multi-objective evolutionary algorithms for the Vertex Cover problem in the context of parameterized complexity. We relate the runtime of our algorithms to the input size and the cost of a minimum solution and point out that the search process of evolutionary algorithms creates partial solutions that are similar to the effect of a kernelization (i.e. a special type of preprocessing from parameterized complexity). Based on this, we show that evolutionary algorithms solve the vertex cover problem efficiently if the size of a minimum vertex cover is not too large, i.e. the expected runtime is bounded by O(f(OPT) $n^{c}),$ where c is a constant and f a function that only depends on OPT. This shows that evolutionary algorithms are randomized fixed-parameter tractable algorithms for the vertex cover problem. |
| Starting Page | 293 |
| Ending Page | 300 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781605583259 |
| DOI | 10.1145/1569901.1569943 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-07-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Runtime analysis Multi-objective optimization Evolutionary algorithms Combinatorial optimization |
| Content Type | Text |
| Resource Type | Article |
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