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Fitness approximation[1] aims to approximate the objective or fitness functions in evolutionary optimization by building up machine learning models based on data collected from numerical simulations or physical experiments. The machine learning models for fitness approximation are also known as meta-models or surrogates, and evolutionary optimization based on approximated fitness evaluations are also known as surrogate-assisted evolutionary approximation.[2] Fitness approximation in evolutionary optimization can be seen as a sub-area of data-driven evolutionary optimization.[3]
- ^ Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9:3–12, 2005
- ^ Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2):61–70, 2011
- ^ Y. Jin, H. Wang, T. Chugh, D. Guo and K. Miettinen. Data-driven evolutionary optimization -- An Overview and Case Studies or black-box optimization. 23(3):442-459, 2019