Is it worth to approximate fitness by machine learning?: investigation on the extensibility according to problem size

By Dong-Pil Yu, Yong-Hyuk Kim. 2018

4 citations recorded by the source corpus.

It is usual to need an approximate model in evolutionary computation when fitness function is deemed to be abstract or expected to have a long computation time. In these cases, research on possibility of fitness approximation should proceed before applying an evolutionary algorithm in real-world problems. In this paper, it was found that we could train machine learning algorithms with the sampled solutions when problem size is large, if there is a possibility of fitness approximation at small problem sizes.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

Citations in the corpus

• Evolving Robust Neural Architectures to Defend from Adversarial Attacks (2019, neutral): “It is already shown in [Yu and Kim, 2018] that, if there is a possibility of fitness approximation at small search spaces, we could evolve algorithms in an ample search space.”