RoverDevKit is an open physics-based evaluator for lunar micro-rover conceptual design that runs in 30 ms and uses NSGA-II to identify mission-dependent optimal wheel configurations and binding trades.
A fast and elitistmultiobjectivegeneticalgorithm:NSGA-II
39 Pith papers cite this work, alongside 47,759 external citations. Polarity classification is still indexing.
39
Pith papers citing it
47.8k
external citations · external index
citation-role summary
background 2
baseline 1
method 1
citation-polarity summary
representative citing papers
A co-evolutionary method evolves LLM prompts and circuits to produce 8-bit approximate multipliers with better error-area trade-offs than EvoApproxLib.
COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
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.
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.
EvoPref applies NSGA-II evolutionary optimization with archive-based diversity to populations of LoRA adapters, yielding 18% higher preference coverage and 47% lower collapse than gradient descent baselines while matching alignment quality.
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.
A genetic algorithm optimizes weighted combinations of LLM-perceived harm mitigation, expert costs, and participatory scores over stakeholder-action pairs to surface viable AI policy packages for media harms.
Equal reward weighting outperforms targeted weighting in RL-based BPMN generation across 48 configurations, with design choices producing effects as large as applying RL itself.
PGDS is a new explainable AI method for many-objective optimization that automates target selection via partitioning and identifies influential decision variables through distance-based sensitivity analysis.
A methodology is proposed to integrate Life Cycle Assessment as an additional discipline in Multidisciplinary Design Analysis and Optimization frameworks for launch vehicle eco-design, demonstrated through multi-objective optimization on an expendable launcher.
Adjusting Jansen linkage lengths within 29% yields designs that flatten stance by 28%, smooth velocity by 58%, and cut total joint wear by 56% while satisfying gait constraints.
TCP-MCP co-evolves prompts and topologies for multi-agent systems, reporting 82.66-96.61% accuracy on MMLU-Pro/MMLU/GSM8K while using up to 5.69x fewer tokens than debate baselines.
Develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well portfolio selection under uncertainty, solved via sample average approximation and NSGA-II.
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
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.
CHAMB-GA is a containerized microservice framework that scales genetic algorithms from laptops to 3,500+ cores on Kubernetes and SLURM with low overhead.
Describes an LLM-orchestrated workflow that trains a surrogate on CAE data (R²=0.87), runs NSGA-II optimization, generates morphed geometries, and produces 35 compliant pedestrian-protection designs in a front-bumper case study.
citing papers explorer
-
RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
RoverDevKit is an open physics-based evaluator for lunar micro-rover conceptual design that runs in 30 ms and uses NSGA-II to identify mission-dependent optimal wheel configurations and binding trades.
-
Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
A co-evolutionary method evolves LLM prompts and circuits to produce 8-bit approximate multipliers with better error-area trade-offs than EvoApproxLib.
-
Constituency Optimisation Through Hamiltonian Representation Of Mandates (COTHROM): Algorithmic Redistricting of Irish Election Boundaries
COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
-
Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
-
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.
-
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.
-
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.
-
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.
-
EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
EvoPref applies NSGA-II evolutionary optimization with archive-based diversity to populations of LoRA adapters, yielding 18% higher preference coverage and 47% lower collapse than gradient descent baselines while matching alignment quality.
-
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.
-
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.
-
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.
-
Informing AI Policy Assessment using Large-Scale Simulation of Interventions
A genetic algorithm optimizes weighted combinations of LLM-perceived harm mitigation, expert costs, and participatory scores over stakeholder-action pairs to surface viable AI policy packages for media harms.
-
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Equal reward weighting outperforms targeted weighting in RL-based BPMN generation across 48 configurations, with design choices producing effects as large as applying RL itself.
-
Partition-Guided Distance Saliency: Bridging Decision and Objective Spaces in Many-Objective Optimization
PGDS is a new explainable AI method for many-objective optimization that automates target selection via partitioning and identifies influential decision variables through distance-based sensitivity analysis.
-
A Methodology for Integrating Life Cycle Assessment into a Multidisciplinary Design Analysis and Optimization Framework for Sustainable Launcher Development
A methodology is proposed to integrate Life Cycle Assessment as an additional discipline in Multidisciplinary Design Analysis and Optimization frameworks for launch vehicle eco-design, demonstrated through multi-objective optimization on an expendable launcher.
-
Durability-Aware Multi-Objective Optimization of the Jansen Linkage: Trading Gait Quality Against Joint Wear
Adjusting Jansen linkage lengths within 29% yields designs that flatten stance by 28%, smooth velocity by 58%, and cut total joint wear by 56% while satisfying gait constraints.
-
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
TCP-MCP co-evolves prompts and topologies for multi-agent systems, reporting 82.66-96.61% accuracy on MMLU-Pro/MMLU/GSM8K while using up to 5.69x fewer tokens than debate baselines.
-
A two-stage stochastic programming framework for oil and gas exploration well portfolio optimization under geological and economic uncertainty
Develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well portfolio selection under uncertainty, solved via sample average approximation and NSGA-II.
-
Physics Guided Generative Optimization for Trotter Suzuki Decomposition
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
-
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.
-
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.
-
CHAMB-GA: A Containerized HPC Scalable Microservice-Based Framework for Genetic Algorithms
CHAMB-GA is a containerized microservice framework that scales genetic algorithms from laptops to 3,500+ cores on Kubernetes and SLURM with low overhead.
-
Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow
Describes an LLM-orchestrated workflow that trains a surrogate on CAE data (R²=0.87), runs NSGA-II optimization, generates morphed geometries, and produces 35 compliant pedestrian-protection designs in a front-bumper case study.
-
Automated Repair of Requirements for Cyber-Physical Systems in Simulink Requirements Tables
A framework repairs CPS requirements in Simulink by leveraging system execution data and is evaluated as effective on six real-world case studies covering 12 requirements.
-
PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
Physics-informed active learning optimizes tri-gate FinFETs, identifying designs with 2x better switching efficiency and 3.3 A drive current in multi-fin setups.
-
A quantum-like benchmark for context-sensitive associative memory with adaptive plasticity
A controlled benchmark for context-sensitive memory shows adaptive plasticity (especially homeostatic) enables recall under weak support, with quantum-like models preserving order sensitivity better than Markov controls but without universal advantage.
-
Reinforcement Learning for Laser Additive Manufacturing Scan-Order Optimisation: A Bilevel Proxy--FEA Diagnostic Framework for Reward and World-Model Diagnosis
A bilevel Proxy-FEA diagnostic framework is introduced and tested on a simplified LDED32 stripe benchmark to reveal proxy misalignment with FEA labels and a stress-distortion trade-off in RL-guided scan-order optimization.
-
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.
-
Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
A GNN-based DRL model with two actor-critics produces comparable Pareto fronts for multi-objective fog application placement in milliseconds versus hours for genetic algorithms.
-
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.
-
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.
-
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.
-
Feature weighting for data analysis via evolutionary simulation
A replicator-type feature-weighting update is claimed to converge globally to weights proportional to 1/(column mean + 1/2), but the convergence proof has a critical gap.
-
On Parallel and Batch-Cutting Strategies for Norm-Minimization-Based Convex Vector Optimization
Introduces parallel subproblem evaluation and batch addition of up to K cuts per iteration for a convex vector optimization algorithm, proves the batch variant preserves the O(k^{2/(1-q)}) convergence rate, and reports 62-80% fewer iterations with variable wall-clock gains.
-
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.
-
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.
-
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.
-
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.