Final Productive Fitness for Surrogates in Evolutionary Algorithms

Maximilian Zorn, Sarah Gerner, Philipp Altmann, Thomas Gabor

Abstract: Final productive fitness is an a posteriori fitness estimate for evolutionary algorithms that takes into account the fitness of an individual's descendants. We use that metric in the context of surrogate-based evolutionary algorithms to show that computing a surrogate not for the original objective function but for the final productive fitness based on said objective function improves optimization properties of the surrogate function and might thus be a useful tool for certain dynamic optimization problems.