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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative 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.
A replicator-type dynamic on the standard simplex for feature weights from a normalized data matrix converges globally to a unique interior equilibrium.
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.
-
Feature weighting for data analysis via evolutionary simulation
A replicator-type dynamic on the standard simplex for feature weights from a normalized data matrix converges globally to a unique interior equilibrium.