On the Application of Multiple-Deme Parallel Genetic Algorithms in Astrophysics

We present a Parallel Genetic Algorithm (PGA) for the solution of a constrained global optimization problem arising in the detection of gravitational waves through the matched filter technique. This is a hard problem, since it has a black-box stochastic objective function, which is highly nonlinear, multiextremal and computationally expensive. Our PGA uses multiple subpopulations (demes) that evolve separately by the application of genetic operators tailored to the optimization problem; individuals are exchanged from time to time through a suitable migration mechanism. Numerical experiments performed on a set of representative test problems show that the PGA is able to solve the problem with the same accuracy and reliability as the grid search, which is the reference algorithm for this problem, but requiring a smaller execution time.