  Optimization problems find widespread use in both single-objective and
multi-objective scenarios. In practical applications, users aspire for
solutions that converge to the region of interest (ROI) along the Pareto front
(PF). While the conventional approach involves approximating a fitness function
or an objective function to reflect user preferences, this paper explores an
alternative avenue. Specifically, we aim to discover a method that sidesteps
the need for calculating the fitness function, relying solely on human
feedback. Our proposed approach entails conducting direct preference learning
facilitated by an active dueling bandit algorithm. The experimental phase is
structured into three sessions. Firstly, we assess the performance of our
active dueling bandit algorithm. Secondly, we implement our proposed method
within the context of Multi-objective Evolutionary Algorithms (MOEAs). Finally,
we deploy our method in a practical problem, specifically in protein structure
prediction (PSP). This research presents a novel interactive preference-based
MOEA framework that not only addresses the limitations of traditional
techniques but also unveils new possibilities for optimization problems.
