Efficient use of partially converged simulations in evolutionary optimization

For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model. This makes it challenging to use evolutionary algorithms which usually have to evaluate thousands of solutions before converging. On the other hand, in many cases, even a prematurely stopped run of the simulation may serve as a cheaper, albeit less accurate (low fidelity), estimate of the true fitness value. For evolutionary optimization, this opens up the opportunity to decide about the simulation run length for each individual. In this paper, we propose a mechanism that is capable of learning the appropriate simulation run length for each solution. To test our approach, we propose two new benchmark problems, one simple artificial benchmark function and one benchmark based on a computational fluid dynamics simulation scenario to design a toy submarine. As we demonstrate, our proposed algorithm finds good solutions much faster than always using the full computational fluid dynamics simulation and provides much better solution quality than a strategy of progressively increasing the fidelity level over the course of optimization