A genetic algorithm for a global optimization problem arising in the detection of gravitational waves
- 129 Downloads
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.
KeywordsGlobal optimization Genetic algorithm Detection of gravitational waves
Unable to display preview. Download preview PDF.
- 1.Thorne K.S.: Gravitational radiation. In: Hawking, S.W., Israel, W. (eds) 300 Years of Gravitation, pp. 330–458. Cambridge University Press, Cambridge (1987)Google Scholar
- 7.Papoulis A.: Probability, Random Variables, and Stochastic Processes. 3rd edn. McGraw-Hill, New York (1991)Google Scholar
- 10.Allen, B., et al.: LAL Software Documentation. Revision 1.44 (2005). Available at http://www.lsc-group.phys.uwm.edu/lal/
- 13.Holland J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 14.Michalewicz Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer, New York (1998)Google Scholar
- 17.Bäck, T., Fogel, D.B., Michalewicz, Z. (eds): Evolutionary Computation 1: Basic Algorithms and Operators. IOP Publishing, Bristol (2000)Google Scholar
- 18.De Jong K.A.: Evolutionary Computation: A Unified Approach. MIT press, Cambridge (2006)Google Scholar
- 23.Back T.: Mutation parameters. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds) Evolutionary Computation 2: Advanced Algorithms and Operators, pp. 142–151. IOP Publishing, Bristol (2000)Google Scholar
- 24.Vajda, P., Eiben, A., Hordijk, W.: Parameter control methods for selection operators in genetic algorithms. In: Parallel problem solving from nature—PPSN X, Lecture Notes in Computer Science, pp. 620–630. Springer, Berlin/Heidelberg (2008)Google Scholar
- 28.Liuzzi, G., Lucidi, S., Piccialli, V.: A direct-based approach exploiting local minimizations for the solution of large-scale global optimization problems. Comput. Optim. Appl. (2008)Google Scholar
- 30.Strongin R.G., Sergeyev Y.D.: Global Optimization with Non-Convex Constraints Sequential and Parallel Algorithms, Nonconvex Optimization and its Applications vol. 45. Kluwer, Dordrecht (2000)Google Scholar