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

A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution