A competitive coevolution scheme inspired by DE

A competitive coevolutionary algorithm is used as a metaheuristic for making a combination of optimization algorithms more robust against poorly chosen starting values. Another objective of the coevolutionary algorithm is to minimize the computation time while still achieving convergence. Two scenarios are created. The species in the coevolution are parameters for the optimization procedure (called predators) and parameters defining starting points for the optimization algorithms (called prey). Two functions are considered for the prey and two algorithms are explored for the predators, namely simulated annealing and BFGS. The creation and selection of new individuals in the coevolution is done analogously to that of DE. The historical evolution of the prey is explored as a potential diagnostics tool for multimodality.