DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
MOEA/D: A multiobjective evolutionary algorithm based on decomposition.IEEE Transactions on Evolutionary Computation, 11(6):712–731
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An evolutionary search framework auto-configures multi-scale bi-branch CNNs to generate Pareto fronts of error-versus-complexity models for multi-output time-series forecasting.
citing papers explorer
-
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
-
Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting
An evolutionary search framework auto-configures multi-scale bi-branch CNNs to generate Pareto fronts of error-versus-complexity models for multi-output time-series forecasting.