Multiobjective Optimization
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Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FeiLiu36/LLM4MOEA
공식 코드Large Language Model for Multi-objective Evolutionary Optimization
https://github.com/Xi-L/ParetoMTL
공식 코드https://github.com/aamakor/continuation-method
공식 코드A multiobjective continuation method to compute the regularization path of deep neural networks
https://github.com/cavalab/fomo
공식 코드Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
https://github.com/csdlpsu/qpots
공식 코드qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
https://github.com/dockstring/dockstring
공식 코드DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
https://github.com/ehw-fit/tf-approximate
공식 코드ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
https://github.com/emi-group/evomo
공식 코드Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
https://github.com/emi-group/evoxbench
공식 코드https://github.com/emi-group/tensorrvea
공식 코드GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
https://github.com/google-research/google-research
공식 코드ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
https://github.com/hewlettpackard/dc-rl
공식 코드https://github.com/iNet-WZU/CMOMPA
공식 코드https://github.com/jamartinh/polcanet
공식 코드https://github.com/liuj-2023/invtremo
공식 코드Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization
https://github.com/malena1906/Pruning-Weights-with-Biobjective-Optimization-Keras
공식 코드Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
https://github.com/maxjcohen/transformer
공식 코드End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
https://github.com/mihaioltean/traceless-genetic-programming
공식 코드Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
https://github.com/milabhitsz/2022sheoptmpnds3
공식 코드https://github.com/mit-gfx/ContinuousParetoMTL
공식 코드Efficient Continuous Pareto Exploration in Multi-Task Learning
https://github.com/numbbo/coco
공식 코드COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/parmoo/cfr-materials
공식 코드https://github.com/sanphiee/MOT-sGPLDA-MCE18
공식 코드Multiobjective Optimization Training of PLDA for Speaker Verification
https://github.com/sanphiee/MOT-sGPLDA-SRE14
공식 코드Multiobjective Optimization Training of PLDA for Speaker Verification
https://github.com/tailintalent/distillation
공식 코드Pareto-optimal data compression for binary classification tasks
https://github.com/tanmay-ty/tntrules
공식 코드https://github.com/taochen/sbse-qi
공식 코드https://github.com/wangronin/hypervolumederivatives
공식 코드https://github.com/xzhang2523/libmoon
공식 코드LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
https://github.com/zhangzizhen/ml-dam
공식 코드Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
https://github.com/JohnYKiyo/coco_trial
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/amkall/tcc_coco
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/code-implementation1/Code1/tree/main/EF-ENAS
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
https://github.com/emi-group/evox
Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
https://github.com/lamda-bbo/offline-moo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.