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| Content Provider | ACM Digital Library |
|---|---|
| Author | Wagner, Markus Zarges, Christine Lissovoi, Andrei Sudholt, Dirk |
| Abstract | Bet-and-run initialisation strategies have been experimentally shown to be beneficial on classical NP-complete problems such as the travelling salesperson problem and minimum vertex cover. We analyse the performance of a bet-and-run restart strategy, where k independent islands run in parallel for $t_{1}$ iterations, after which the optimisation process continues on only the best-performing island. We define a family of pseudo-Boolean functions, consisting of a plateau and a slope, as an abstraction of real fitness landscapes with promising and deceptive regions. The plateau shows a high fitness, but does not allow for further progression, whereas the slope has a low fitness initially, but does lead to the global optimum. We show that bet-and-run strategies with non-trivial k and $t_{1}$ are necessary to find the global optimum efficiently. We show that the choice of $t_{1}$ is linked to properties of the function. Finally, we provide a fixed budget analysis to guide selection of the bet-and-run parameters to maximise expected fitness after t = k · $t_{1}$ + $t_{2}$ fitness evaluations. |
| Starting Page | 857 |
| Ending Page | 864 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450349208 |
| DOI | 10.1145/3071178.3071329 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-07-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Local search Optimisation Stochastic search Restarts Initialisation |
| Content Type | Text |
| Resource Type | Article |
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