Transformers as Surrogate Models for Genetic Programming in AutoML Tasks

This is the authors' abstract. We don't add key points for this paper.

In applications where the fitness function has a high computational cost, one of the main drawbacks of Evolutionary Algorithms when compared to other search methods is a prohibitive computational cost. The use of surrogates as proxies for fitness function calculation to alleviate this problem is not new, but addressing the problem as a binary relation learning, i.e., evaluating if one individual is better or worse than another without estimating the actual value of the fitness, is a recent trend.