Advanced and Classical Selection Methods in Genetic Algorithms: A Comprehensive Comparative Analysis

Abstract

Selection mechanisms critically influence the convergence behavior and solution quality of Genetic Algorithms (GAs). This study presents a rigorous empirical comparison of six selection methods: three classical methods—Random Selection, Roulette Wheel Selection (RWS), and Tournament Selection (TS)—and three adaptive methods: Fitness-Distance Balance (FDB), Dynamic FDB (dFDB), and Functional Weight-based Selection (FW). Experiments were conducted across 23 classical benchmark functions (F1–F23) and 10 CEC2019 functions (cec01–cec10), with each configuration executed 30 times using consistent GA parameters. Performance was assessed using Best, Mean, Median, and Standard Deviation, with statistical significance determined by the Wilcoxon rank-sum test (α = 0.05). The results reveal that TS consistently achieved the best or statistically equivalent performance in 30 out of 33 functions, outperforming both classical and adaptive alternatives. Notably, RWS showed surprising competitiveness, outperforming adaptive methods such as FDB and dFDB in several scenarios. While dFDB and FW improved over static FDB, they failed to consistently outperform TS. These findings confirm TS as a robust default choice for diverse optimization landscapes and provide new empirical evidence regarding the limited practical advantage of current adaptive strategies within GAs. This study contributes the first controlled GA-based evaluation of adaptive selection mechanisms on both classical and CEC2019 benchmarks, offering insights for practitioners designing efficient evolutionary systems. Limitations related to fixed GA settings, function diversity, and adaptive method complexity are acknowledged, and future work is suggested to explore hybrid and problem-aware selection strategies.