Code for "A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications"
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Updated
Aug 11, 2024 - Python
Code for "A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications"
DVFS framework
Simple multi-objective optimisation routine based on Celery
Genetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)
AutoOED: Automated Optimal Experimental Design Platform
📉 Disk Storage of Compressed k-mer Dictionaries, with or without Random Access in Main Memory.
Master project. Simulator to find the optimal deployment model of FaaS (serverless) and VM-based instances to reduce cost
🎓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.
Implementation of verification algorithms for the Pareto-Rational Verification problem (PRV problem).
[ICML 2020] Efficient Continuous Pareto Exploration in Multi-Task Learning
MOEA image segmentation with NSGA-II and weighted sum GA
This 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 trad…
Multi-objective optimization based on sloping plate optimization algorithm called Multi-objective Inclined Planes system optimization algorithm (MOIPO) is presented in this link. The proposed method uses the concept of Pareto optimization to identify non-dominant positions and an external tank to maintain these positions.
Finding Pareto Optimal Solutions in Large Graphs Using Graph Databases
NSGA-III: Non-dominated Sorting Genetic Algorithm, the Third Version — MATLAB Implementation
Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) in MATLAB
Code for calibration as a method of design.
Finding optimal no of clusters in MOPSO implementation of Wireless Sensor Networks.
which kart setups are good
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