Tuning genetic algorithms for underwater acoustics using a priori statistical information

We present a new technique for the evaluation/selection procedures of genetic algorithms, to be used in the context of parameter estimation problems. The proposed algorithm uses a priori information about the structure of the surface of which an extremum is being searched. For parameter estimation problems, the availability, at each iteration of a genetic algorithm, of a collection of samples of the ambiguity surface of the problem, enables the determination of the correlation between the observed ambiguity surface (at the sampled points) and the predicted ambiguity surface. The consideration of this information allows early detection of secondary extrema (which yield an ambiguity surface which does not correlate well with the observed one) and thus contributes to speed the convergence of the algorithm to the global optimal values. The paper applies the proposed technique to a source localization problem