A PyTorch Library for Multi-Task Learning

A framework for single/multi-objective optimization with metaheuristics

syftr is an agent optimizer that helps you find the best agentic workflows for your budget.

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

Official PyTorch Implementation for Conflict-Averse Gradient Descent (CAGrad)

LibMOON is a standard and flexible framework to study gradient-based multiobjective optimization.

EvoMO is a GPU-accelerated library for evolutionary multiobjective optimization (EMO)

Python library for parallel multiobjective simulation optimization

L2O/NCO codes from CIAM Group at SUSTech, Shenzhen, China

A Python implementation of the decomposition based multi-objective evolutionary algorithm (MOEA/D)

Capacitated vehicle routing problem implemented in python using DEAP package. Non dominated sorting Genetic algorithm is used to solve Multiobjective problem of minimizing Total distance travelled by all vehicles and minimizing total number of vehicles at same time.

Multiobjective black-box optimization using gradient-boosted trees

"Hierarchical Reinforcement Learning for Integrated Recommendation" (AAAI 2021) https://ojs.aaai.org/index.php/AAAI/article/view/16580

🧬 Modularised Evolutionary Algorithms For Python with Optional JIT and Multiprocessing (Ray) support. Inspired by PyTorch Lightning

hybrid genetic algorithm for container loading problem

constrained/unconstrained multi-objective bayesian optimization package.

This is the official implementation for COSMOS: a method to learn Pareto fronts that scales to large datasets and deep models.

An open source framework for interactive multiobjective optimization methods

Genetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)

Lightweight tools for experimental design and multi-objective optimization.