Common pitfalls to avoid while using multiobjective optimization in machine learning

Understand

Recently, there has been an increasing interest in the application of multiobjective optimization (MOO) in machine learning (ML).

• This interest is driven by the numerous real-life situations where multiple objectives must be optimized simultaneously.
• A key aspect of MOO is the existence of a Pareto set, rather than a single optimal solution, which represents the optimal trade-offs between different objectives.
• Despite its potential, there is a noticeable lack of satisfactory literature serving as an entry-level guide for ML practitioners aiming to apply MOO effectively.