42 skills found · Page 1 of 2
datarobot / Syftrsyftr is an agent optimizer that helps you find the best agentic workflows for your budget.
mit-gfx / ContinuousParetoMTL[ICML 2020] Efficient Continuous Pareto Exploration in Multi-Task Learning
yunshengtian / AutoOEDAutoOED: Automated Optimal Experimental Design Platform
SirPepe / OrnamentMid-level, pareto-optimal, treeshakable and tiny (< 5k) TypeScript-positive toolkit for web component infrastructure
dbmptr / EPOSearchExact Pareto Optimal solutions for preference based Multi-Objective Optimization
smkalami / Ypea126 Nsga3NSGA-III: Non-dominated Sorting Genetic Algorithm, the Third Version — MATLAB Implementation
EmilioSchi / Niched Pareto Genetic Algorithm NPGAGenetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)
michstrauch / CoMOLACoMOLA is a generic python tool for Constrained Multi-objective Optimization of Land use Allocation. It offers a framework to explore a landscape’s potential for ecosystem service supply and biodiversity conservation considering land use conversion and areal coverage constraints. CoMOLA can be used immediately by inputting a raster map representing the status-quo land use, ready-to-run models written in R including their input data, and (optional) information on constraints. The end product is a set of pareto-optimal solutions (land use maps) from which decision makers can discuss and select appropriate solutions according to their preferences.
helm-compiler / Carpentry CompilerOur carpentry compiler converts high-level geometric designs made by users to low-level fabrication instructions that can be directly followed to manufacture parts. The compiler performs multi-objective optimization on the low-level instructions to generate Pareto-optimal candidates.
lsinfo3 / PocoPOCO: Pareto-Optimal Controller Placement
TRI-AMDD / PiroSoftware for evaluating pareto-optimal synthesis pathways
kshitija2 / Interactive Multi Objective Reinforcement LearningMulti-objective reinforcement learning deals with finding policies for tasks where there are multiple distinct criteria to optimize for. Since there may be trade-offs between the criteria, there does not necessarily exist a globally best policy; instead, the goal is to find Pareto optimal policies that are the best for certain preference functions. The Pareto Q-learning algorithm looks for all Pareto optimal policies at the same time. Introduced a variant of Pareto Q-learning that asks queries to a user, who is assumed to have an underlying preference function and also the scalarized Q-learning algorithm which reduces the dimensionality of multi-objective space by using scalarization function and ask user preferences by taking weights for scalarization. The goal is to find the optimal policy for that user’s preference function as quickly as possible. Used two benchmark problems i.e. Deep Sea Treasure and Resource Collection for experiments.
rithinch / Pareto Optimal Student Supervisor Allocation🎓An AI tool to assist universities with optimal allocation of students to supervisors for their dissertations. Devised a multi-objective genetic algorithm for the task.
projjal1 / MOPSO WSNFinding optimal no of clusters in MOPSO implementation of Wireless Sensor Networks.
chenlong-clock / RULE Unlearn[NeurIPS25] RULE: Reinforcement UnLEarning Achieves Forge-retain Pareto Optimality
ShriyaBhatija / MO CBOMulti-Objective Causal Bayesian Optimisation, a new paradigm for finding Pareto-optimal interventions in multi-outcome causal models
tuantran23012000 / PHN CSFCode for "A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications"
microsoft / POLARExperiments for "Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision"
teamparodis / ParodisThis is the official repository to PARODIS, the Matlab PAReto Optimal Model Predictive Control framework for DIstributed Systems.
ali-ece / Design Of Optimal CMOS Ring Oscillator Using An Intelligent Optimization ToolThis paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.
