  The search for traveltime parameters is a global optimization problem.
Several metaheuristics have been proposed to locate the global optima to
compute the least amount of their objective functions. However, the theoretical
limitations imposed by the no-free-lunch theorem restrict the optimality of
such metaheuristics. To escape those limitations, we propose a coevolutionary
approach called evolution by neighborhood similarity, which is outside the
scope of the restrictions of this theorem and allows us to speed up the search
convergence. The technique's effectiveness is based on the approach to
exchanging the best individuals found between suitable domains during a
differential evolution metaheuristic execution. Moreover, we further expand our
technique to graphics processing units, allowing us to explore the performance
and memory aspects of the method. Ultimately, our complete coevolutionary
algorithm can speed up the parameter search by more than five times and reduce
the energy consumption by more than thirty-three times compared to a regular
metaheuristic implementation. Although some overheads are still problematic in
the technique, we present a first approach that effectively explores the data
redundancy hidden in the estimation process, allowing us to provide
qualitative, performance, and scalable improvements.
