To address these shortcomings, we introduce SymPcNSGA-Testing (Symbolic execution, Path clustering and NSGA-II Testing), a ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that ...

The final, formatted version of the article will be published soon. This work reports on a pilot study for optimizing the design of a fast neutron irradiation experiment in a thermal neutron spectrum, ...

Abstract: Expensive multi-objective binary optimization problems frequently emerge in real-world applications, where evaluating a single solution incurs significant computational or physical costs.

SLSQP stands for Sequential Least Squares Programming. It is a numerical optimization algorithm used to solve constrained nonlinear optimization problems. In this project, we aim to optimize objective ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing ...

Abstract: Multi-party multi-objective optimization, which aims to find a solution set that satisfies multiple decision makers (DMs) as much as possible, has attracted the attention of researchers ...