Game theory is a powerful tool for analyzing strategic interactions between rational agents and has been widely applied across fields such as economics, biology, and cybersecurity. In this paper, we propose a novel approach for estimating solutions to multiplayer games of simultaneous decision with continuous strategy sets, including those with infinitely many Nash Equilibria. Our method leverages the coevolution of multiple Evolutionary Algorithms (EAs): a single-objective EA models a single-objective player, while a Pareto dominance-based EA represents a multi-objective player. Each EA optimizes its player's strategies (decisions) through iterative gameplay. We analyze the key features that enable the proposed algorithm to estimate a Nash Equilibrium with minimal deviation from the analytical solution (which remains unknown to the algorithm) and to maintain stability near this solution. Experimental results show that the proposed algorithm converges to the nearest equilibrium with appropriate parameter tuning, including the secondary parent/survival selection criterion for the multi-objective EA, the fitness computation method, the mutation distribution index, and the mutation rate.

We present a novel extension to the game of FlipIt, introducing infrastructure costs and their impact on the at-tacker's success rate. This extension results in a simultaneous, continuous, multi-variable, multi-objective game, which we alge-braically solve in the case of periodic strategies. We then propose a novel fitness criterion for Co-Evolutionary Algorithms suitable for estimating Pure Strategy Nash Equilibria for such types of games. Afterward, we estimate solutions to the extended FlipIt game when game outcomes are evaluated both via analytical expectancy expressions and as the average of game simulations. The results demonstrate the effectiveness of the proposed framework for deterministic games and also hint towards its applications in stochastic games like FlipIt and even in broader multi-agent settings.

This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses the shortcomings of previous approaches. A new variant is proposed that exploits, as some previous approaches, metalearning. The method requires that experiments be conducted on few samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was carried out fully. This in turn permits to generate a prediction regards the relative performance of algorithms. Experimental evaluation shows that the method competes well with previous approaches and provides quite good and practical solution to this problem. 1.