NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
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Updated
Dec 23, 2024 - Python
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
A PyTorch Library for Multi-Task Learning
Evolutionary multi-objective optimization platform
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Generalized and Efficient Blackbox Optimization System
🛍 A real-world e-commerce dataset for session-based recommender systems research.
High-performance metaheuristics for optimization coded purely in Julia.
Jupyter/IPython notebooks about evolutionary computation.
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Deep learning toolkit for Drug Design with Pareto-based Multi-Objective optimization in Polypharmacology
Library for Jacobian descent with PyTorch. It enables optimization of neural networks with multiple losses (e.g. multi-task learning).
[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search
Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper
AutoOED: Automated Optimal Experimental Design Platform
Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
Multi-objective Bayesian optimization
Generalized and Efficient Blackbox Optimization System.
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