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| Content Provider | Springer Nature Link |
|---|---|
| Author | di Serafi, Daniela Gomez, Susana Mila, Leopoldo Riccio, Filippo Toraldo, Gerardo |
| Copyright Year | 2010 |
| Abstract | The detection of gravitational waves is a long-awaited event in modern physics and, to achieve this challenging goal, detectors with high sensitivity are being used or are under development. In order to extract gravitational signals emitted by coalescing binary systems of compact objects (neutron stars and/or black holes), from noisy data obtained by interferometric detectors, the matched filter technique is generally used. Its computational kernel is a box-constrained global optimization problem with many local solutions and a highly nonlinear and expensive objective function, whose derivatives are not available. To tackle this problem, we designed a real-coded genetic algorithm that exploits characteristic features of the problem itself; special attention was devoted to the choice of the initial population and of the recombination operator. Computational experiments showed that our algorithm is able to compute a reasonably accurate solution of the optimization problem, requiring a much smaller number of function evaluations than the grid search, which is generally used to solve this problem. Furthermore, the genetic algorithm largely outperforms other global optimization algorithms on significant instances of the problem. |
| Starting Page | 41 |
| Ending Page | 55 |
| Page Count | 15 |
| File Format | |
| ISSN | 09255001 |
| Journal | Journal of Global Optimization |
| Volume Number | 48 |
| Issue Number | 1 |
| e-ISSN | 15732916 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2010-01-30 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Global optimization Genetic algorithm Detection of gravitational waves Optimization Real Functions Computer Science Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Optimization Applied Mathematics Management Science and Operations Research Business, Management and Accounting Computer Science Applications |
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