  Computational cost in metaheuristics such as Evolutionary Algorithms (EAs) is
often a major concern, particularly with their ability to scale. In data-based
training, traditional EAs typically use a significant portion, if not all, of
the dataset for model training and fitness evaluation in each generation. This
makes EAs suffer from high computational costs incurred during the fitness
evaluation of the population, particularly when working with large datasets. To
mitigate this issue, we propose a Machine Learning (ML)-driven Distance-based
Selection (DBS) algorithm that reduces the fitness evaluation time by
optimizing test cases. We test our algorithm by applying it to 24 benchmark
problems from Symbolic Regression (SR) and digital circuit domains and then
using Grammatical Evolution (GE) to train models using the reduced dataset. We
use GE to test DBS on SR and produce a system flexible enough to test it on
digital circuit problems further. The quality of the solutions is tested and
compared against the conventional training method to measure the coverage of
training data selected using DBS, i.e., how well the subset matches the
statistical properties of the entire dataset. Moreover, the effect of optimized
training data on run time and the effective size of the evolved solutions is
analyzed. Experimental and statistical evaluations of the results show our
method empowered GE to yield superior or comparable solutions to the baseline
(using the full datasets) with smaller sizes and demonstrates computational
efficiency in terms of speed.
