A comparison of the performance of classical methods and genetic algorithms for optimization problems involving numerical models

All test problems in the optimization and genetic algorithm (GA) literature involve analytical objective functions, which can be calculated exactly (to within floating point accuracy) using elementary operations and functions. However, almost al practical chemical engineering optimization problems involve sets of nonlinear equations or ordinary or partial differential equations that must be solved by some numerical methods (iterative root finding, finite differences, Rung Kutta, etc.) which inherent rounding and truncation errors. It is suspected that evolutionary methods such as genetic algorithms are better than classical deterministic methods for these problems. This paper aims to test this hypothesis by comparing the performance of two classical deterministic methods and a GA method on some representative engineering problems.