A Julia package for solving multi-objective optimization problems with composite structure (F = f + h). Implements Conditional Gradient, Proximal Gradient, and Partially Derivative-Free algorithms that operate directly on the vector-valued objective, without scalarization or heuristics (direct / vector-optimization methods).

Implementation of Penalty-Decomposition Derivative-Free method for the Minimization of Partially Separable functions

Methods for derivative-free optimization of continuous functions

Offical Pytorch Implementation of EnKG

A simple Python-3 implementation of the derivative-free Torczon algorithm for nonlinear constrained optimization

A julia implementation of the CMA Evolution Strategy for derivative-free optimization of potentially non-linear, non-convex or noisy functions over continuous domains.

Derivative-free solver for the minimization of a function over the convex hull of a set of vectors

A python package of Zeroth-Order Optimization (ZOOpt)

Client of distributed ZOOpt in Julia

Paper: Challenges in High-dimensional Reinforcement Learning with Evolution Strategies

Tutorial on Bayesian optimization in R

A collection of black-box optimizers with a focus on evolutionary algorithms

Derivative-free nonlinear global optimizer with python interface

The Stepping Stones Search Algorithm. A simple derivative-free optimizer.

Constrained multi-objective derivative-free global solver

Code for Multi-Objective Simultaneous Optimistic Optimisation