Jiayu Liang

Papers

Total Citations

H-Index

About

Jiayu Liang is a researcher in evolutionary computation and genetic programming, with a focus on bloat control and multi-objective optimization. Their most-cited work, "Preference-driven Pareto front exploitation for bloat control in genetic programming" (2020, 9 citations), introduces a novel approach to managing code growth—a persistent challenge in genetic programming. By leveraging Pareto front exploitation guided by user preferences, Liang’s method balances solution accuracy with program size, enabling more efficient and interpretable evolutionary models. This contribution addresses a critical trade-off in the field, offering practical tools for applications ranging from symbolic regression to automated design. Liang’s research advances the understanding of how preference-based optimization can enhance evolutionary algorithms, making them more adaptable to real-world constraints. With a growing citation impact, their work is gaining recognition among peers working on complexity control and multi-criteria decision-making in evolutionary systems. Liang’s approach stands out for its integration of user-defined priorities, bridging the gap between algorithmic efficiency and practical usability. As their research continues to evolve, Jiayu Liang is poised to make further strides in refining evolutionary techniques for complex, resource-sensitive domains.