Nonsmooth gradient ascent on layered hypervolume and magnitude indicators moves sets to the Pareto front.
Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
Proposes a scalable benchmark for DMOPs with changing objective counts by dynamically selecting subsets from fixed Minus-DTLZ and Minus-WFG problems to isolate the effect of objective number dynamics.
Proposes grading by Pareto surplus over a declared AI baseline to certify AI-resilient student performance in CS courses.
citing papers explorer
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Nonsmooth Set-Gradient Ascent to the Pareto Front via Layered Hypervolume and Magnitude Indicators
Nonsmooth gradient ascent on layered hypervolume and magnitude indicators moves sets to the Pareto front.
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Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
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A Scalable Benchmark Test Suite for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
Proposes a scalable benchmark for DMOPs with changing objective counts by dynamically selecting subsets from fixed Minus-DTLZ and Minus-WFG problems to isolate the effect of objective number dynamics.
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Toward AI-Resilient Assessment in Computer Science Courses in an AI-Native World
Proposes grading by Pareto surplus over a declared AI baseline to certify AI-resilient student performance in CS courses.