An optimization-based inverse design method discovers metainterfaces achieving custom friction laws including power laws with exponents from 2/3 to 1.35 and bilinear forms, with experimental validation for some cases.
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LLMForge is a NAS framework with Infinite-Head Attention, a Forge-Former surrogate, and Forge-DSE engine that discovers hardware-specific architectures for edge language models, yielding variants with improved accuracy, energy, or latency on different substrates.
DRSR uses Quality-Diversity to produce diverse symbolic regression expressions differing in residual distributions, enabling post-search selection on synthetic and astronomical data.
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.
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.
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
Surrogate-assisted neuroevolution produces Pareto-optimal chlorine dosing policies for water distribution systems that outperform PPO on four practical objectives.
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
CVT archives with learned chemical embeddings improve median global hypervolume and multi-objective quality diversity in NLO molecular design compared to grid-based archives.
Integrating on-orbit servicing into satellite maintenance via an inventory replenishment policy reduces annual costs by up to 14.5 percent and launch costs by 25 percent in a real-scale case study.
Combines evolutionary algorithms and MPC to perform privacy-preserving distributed optimization under time limits, tested on assignment and traveling salesperson problems with optional result obfuscation.
Optimized trajectories allow asteroid mining to supply metals for Mars habitats and rovers, potentially enabling sustainable colonies.
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.
Evolutionary algorithms can discover molecules with improved nonlinear optical properties by simultaneously optimizing hyperpolarizability ratio, HOMO-LUMO gap, polarizability, and energy per atom.
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
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Automated Discovery of Metainterfaces with Tailored Friction Laws
An optimization-based inverse design method discovers metainterfaces achieving custom friction laws including power laws with exponents from 2/3 to 1.35 and bilinear forms, with experimental validation for some cases.
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LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models
LLMForge is a NAS framework with Infinite-Head Attention, a Forge-Former surrogate, and Forge-DSE engine that discovers hardware-specific architectures for edge language models, yielding variants with improved accuracy, energy, or latency on different substrates.
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Diversified Residual Symbolic Regression
DRSR uses Quality-Diversity to produce diverse symbolic regression expressions differing in residual distributions, enabling post-search selection on synthetic and astronomical data.
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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.
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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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GLUE: Coordinating Pre-Trained Generative Models for System-Level Design
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
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Towards symbolic regression for interpretable clinical decision scores
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
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Optimizing Chlorination in Water Distribution Systems via Surrogate-assisted Neuroevolution
Surrogate-assisted neuroevolution produces Pareto-optimal chlorine dosing policies for water distribution systems that outperform PPO on four practical objectives.
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Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.
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Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
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CVT Archives and Chemical Embedding Measures for Multi-Objective Quality Diversity in Molecular Design
CVT archives with learned chemical embeddings improve median global hypervolume and multi-objective quality diversity in NLO molecular design compared to grid-based archives.
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On-Orbit Servicing-Integrated Maintenance Strategy for Satellite Constellation
Integrating on-orbit servicing into satellite maintenance via an inventory replenishment policy reduces annual costs by up to 14.5 percent and launch costs by 25 percent in a real-scale case study.
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Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
Combines evolutionary algorithms and MPC to perform privacy-preserving distributed optimization under time limits, tested on assignment and traveling salesperson problems with optional result obfuscation.
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Asteroid Mining to Sustain a Mars Colony: A Logistics Point of View
Optimized trajectories allow asteroid mining to supply metals for Mars habitats and rovers, potentially enabling sustainable colonies.
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HiMARS: Hybrid multi-objective algorithms for recommender systems
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.
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Multi-Objective Evolutionary Design of Molecules with Enhanced Nonlinear Optical Properties
Evolutionary algorithms can discover molecules with improved nonlinear optical properties by simultaneously optimizing hyperpolarizability ratio, HOMO-LUMO gap, polarizability, and energy per atom.
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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.