Nash genetic algorithms: examples and applications

This article presents both theoretical aspects and experimental results for Nash genetic algorithms. Nash GAs are an alternative for multiple objective optimization as they are an optimization tool based on noncooperative game theory. They are explained in detail, along with the advantages conferred by their equilibrium state. This approach is tested on a few benchmark problems, and some comparisons are made with Pareto GAs, particularly in terms of speed and robustness. The different concepts presented in this paper are then illustrated via experiments on a computational fluid dynamics problem, namely nozzle reconstruction with multiple criteria (subsonic and transonic shocked flows). The overall results are that Nash genetic algorithms offer a fast and robust alternative for multiple objective optimization.