Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter set for a specific classification algorithm (also termed model selection), which minimizes training error and enhances the generalization capability of the algorithm. We present a convergence analysis of the proposed algorithm and employ it in conjunction with selected, well-known ML classifiers, namely, an extreme learning machine (ELM), a support vector machine (SVM) and an echo state network (ESN). The performance is evaluated in terms of classification accuracy and learning time, using a range of benchmark datasets, and compared against grid search as well as evolutionary strategy (ES)-based optimization techniques. An empirical study with different datasets demonstrates improved classification accuracy of SVM, from 2% to 5%, for ESN from 3% to 6%, whereas in the case of ELM from 3% to 9%. Comparative simulation results demonstrate the potential of AIN as an alternative optimizer for shallow ML algorithms.

人工免疫网络（AIN）是一种基于种群的进化算法，受到理论免疫学的启发。它运用了生物免疫系统的思想和隐喻来解决多学科的问题。本文介绍了AIN在优化浅层机器学习（ML）分类算法中的新应用。AIN通过为特定分类算法（也称为模型选择）搜索最佳超参数集来完成此任务，这可以最大程度地减少训练误差并增强算法的泛化能力。我们对提出的算法进行收敛性分析，并将其与选定的知名ML分类器结合使用，这些分类器是极限学习机（ELM），支持向量机（SVM）和回波状态网络（ESN）。使用一系列基准数据集，根据分类准确性和学习时间对性能进行评估，并将其与网格搜索以及基于进化策略（ES）的优化技术进行比较。对不同数据集的实证研究表明，对于SSN，SVM的分类精度从2％提高到5％，ESN从3％提高到6％，而ELM从3％提高到9％。对比仿真结果证明了AIN作为浅层ML算法的替代优化器的潜力。而ELM则为3％至9％。对比仿真结果证明了AIN作为浅层ML算法的替代优化器的潜力。而ELM则为3％至9％。对比仿真结果证明了AIN作为浅层ML算法的替代优化器的潜力。