Body fat percentage is an increasingly popular alternative to Body Mass Index to measure overweight and obesity, offering a more accurate representation of body composition. In this work, we evaluate three evolutionary computation techniques, Grammatical Evolution, Context-Free Grammar Genetic Programming, and Dynamic Structured Grammatical Evolution, to derive an interpretable mathematical expression to estimate the percentage of body fat that are also accurate. Our primary objective is to obtain a model that balances accuracy with explainability, making it useful for clinical and health applications. We compare the performance of the three variants on a public anthropometric dataset and compare the results obtained with the QLattice framework. Experimental results show that grammatical evolution techniques can obtain competitive results in performance and interpretability.

翻译：体脂率作为衡量超重与肥胖的指标，正日益成为身体质量指数的替代方案，因其能更准确地反映身体成分。本研究评估了三种进化计算技术——文法进化、上下文无关文法的遗传编程以及动态结构化文法进化——旨在推导出兼具准确性与可解释性的体脂率估计数学表达式。我们的主要目标是获得一个在准确性与可解释性之间取得平衡的模型，以适用于临床与健康应用场景。我们在公开的人体测量数据集上比较了三种变体的性能，并将所得结果与QLattice框架进行了对比。实验结果表明，文法进化技术在性能与可解释性方面均能获得具有竞争力的结果。