Genetic programming (GP) is an evolutionary computation technique that has been successfully applied to various real-world problems during the last decades. However, over the past decades, several numerical solvers have been proposed in the field of applied mathematics, which are usually efficient for certain classes of system matrices (the coefficient matrix of a linear system). Unfortunately, the optimal solver method depends on the system of equations itself and therefore it is impossible to formulate a single algorithm for this purpose. Since the number of unknowns can be huge in numerous real-world applications, efficient and scalable solvers for such systems are necessary. The computation of a numerical solution often requires solving a system of (non-)linear equations. In computational science and engineering, for example, one tries to model physical phenomena and then to approximate these usually continuous mathematical models numerically. Numerical methods are used in various disciplines to solve problems where an analytical solution does not exist or is difficult to find.
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