Fast Genetic Algorithm for feature selection -- A qualitative approximation approach

arXiv:2404.03996v1 Announce Type: cross
Abstract: Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a new machine learning model. An approximation (also known as meta-model or a surrogate) of the true function can be used in such applications to alleviate the computation cost. In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the …

abstract algorithm algorithms apply approximation arxiv cs.ai cs.lg cs.ne evaluation evolutionary algorithms example feature feature selection fitness function machine machine learning machine learning model meta training type world