An Interactive Evolutionary Design Framework Integrating AHP-CRITIC Hybrid Weighting for Mitigating User Evaluation Noise

To address the imbalance between user evaluation noise and algorithmic autonomy in Interactive Evolutionary Design (IED), this study proposes an optimization method integrating subjective and objective weights to alleviate user fatigue and enhance evolutionary efficiency of Genetic Algorithms (GAs). Building upon the Interactive Genetic Algorithm (IGA), an AHP-CRITIC combined proxy model is introduced, leveraging the bidirectional complementarity of AHP and CRITIC to suppress evaluation noise. An interactive evolutionary design model integrating AHP-CRITIC-IGA is constructed, with armchair design as a case study. Compared to traditional IGA, the combined-weighting-based IGA shows faster convergence and more stable fitness trajectories under the same parameter settings. To avoid over-interpretation, evaluation-noise claims are tied to explicit variability metrics (e.g., fitness standard deviation and inter-rater disagreement) reported in the revised experimental section. The proposed method effectively balances the contradiction between human interaction and algorithmic autonomy in interactive evolutionary design. Furthermore, the framework is inherently generalizable and demonstrates significant potential for adaptation and electronic system design, where optimizing complex, multi-criteria problems under user feedback is paramount. This establishes its relevance to the broader field of intelligent and interactive engineering systems.