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 computational
issues arising from using Genetic Algorithm (GA) for feature selection in a
wrapper setting for large datasets. We define 'Approximation Usefulness' to
capture the necessary conditions to ensure correctness of the EA computations
when an approximation is used. Based on this definition, we propose a procedure
to construct a lightweight qualitative meta-model by the active selection of
data instances. We then use a meta-model to carry out the feature selection
task. We apply this procedure to the GA-based algorithm CHC (Cross generational
elitist selection, Heterogeneous recombination and Cataclysmic mutation) to
create a Qualitative approXimations variant, CHCQX. We show that CHCQX
converges faster to feature subset solutions of significantly higher accuracy
(as compared to CHC), particularly for large datasets with over 100K instances.
We also demonstrate the applicability of the thinking behind our approach more
broadly to Swarm Intelligence (SI), another branch of the Evolutionary
Computation (EC) paradigm with results of PSOQX, a qualitative approximation
adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository
with the complete implementation is available.

本文提出了一种两阶段的辅助代理进化方法，用于解决在大型数据集的封装设置中使用遗传算法进行特征选择时产生的计算问题。通过构建轻量级的定性元模型通过主动选择数据实例进行特征选择任务，并在遗传算法 CHC 的基础上应用该过程来创建定性近似变体 CHCQX。实验证明，CHCQX 对于包含超过 100K 个实例的大型数据集收敛速度更快且准确度更高。此外，我们还展示了我们的方法在粒子群优化（PSO）方法的定性逼近适应中的适用性，该方法属于进化计算（EC）范式的另一个分支，相关结果可在 GitHub 上找到完整的实现。