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 having 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, which 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, carried out on a representative test set, showed that the genetic algorithm is able to compute a reasonably accurate solution of the optimization problem and that its success is strongly dependent on a problem-driven choice of the initial population. Furthermore, the genetic algorithm requires a much smaller number of function evaluations than the grid search, which is the algorithm generally used to solve the optimization problem at hand.
Preprint n. 12/2008, Department of Mathematics, Second University of Naples, Caserta, Italy, October 2008