NSGA-II
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Related Articles from SNS
Speeding Up the NSGA-II via Dynamic Population Sizes
arXiv:2509.01739v3 Announce Type: replace Abstract: Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision.
Speeding Up the NSGA-II via Dynamic Population Sizes
Announce Type: replace Abstract: Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision.
Proven Advantage of Multiobjective Evolutionary Algorithms for Problems with Different Degrees of Conflict
arXiv:2408.04207v3 Announce Type: replace Abstract: The field of multiobjective evolutionary algorithms (MOEAs) often emphasizes its popularity for optimization problems with conflicting objectives. However, it is still theoretically unknown how MOEAs perform compared with typical approaches outside this field. This paper conducts such a systematic theoretical comparison on problem classes with different degrees of conflict.
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
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Surrogate Neural Architecture Codesign Package (SNAC-Pack)
arXiv:2605.16138v2 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture...
Adaptive Model Predictive Control of Nonlinear Generic Urban Air Mobility Using Linear Parameter-Varying Systems
arXiv:2606.08836v1 Announce Type: new Abstract: This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter-varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time-varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically...