The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics.

中文翻译：

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遗传算法的新选择算子，可在过早收敛和种群多样性之间取得平衡

研究目标是在遗传算法（GA）方面找到早熟收敛与种群多样性之间的平衡。我们提出了一种新的选择方案，即针对GA的基于拆分的选择（SBS），该方案可确保在两个极端（即勘探和开发）之间实现良好的平衡。将提出的选择算子与五个常用的现有选择算子进行比较。进行了严格的基于模拟的调查，以探索所提出程序的统计特征。此外，通过考虑来自旅行推销员问题库（TSPLIB）的14种不同基准，对竞争方法进行了拟议方案的性能评估。根据t检验统计量和性能指标（PI），