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.

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

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.

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

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.

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

Multi-objective Bayesian optimization

Generalized and Efficient Blackbox Optimization System.