NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

A PyTorch Library for Multi-Task Learning

Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

Evolutionary multi-objective optimization platform

A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

Towards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

Library for Jacobian descent with PyTorch. It enables the optimization of neural networks with multiple losses (e.g. multi-task learning).

🛍 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.

Deep learning toolkit for Drug Design with Pareto-based Multi-Objective optimization in Polypharmacology

Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper

[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.

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.

A comprehensive list of gradient-based multi-objective optimization algorithms in deep learning.

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Multi-objective Bayesian optimization