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).

Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.

[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper

AutoOED: Automated Optimal Experimental Design Platform

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

Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.

Generalized and Efficient Blackbox Optimization System.

Multi-objective Bayesian optimization