Gradient-Free-Optimizers VS surrogate-models

Compare Gradient-Free-Optimizers vs surrogate-models and see what are their differences.

Gradient-Free-Optimizers

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces. (by SimonBlanke)
Optimization gradient-free-optimization bayesian-optimization particle-swarm-optimization evolution-strategies simulated-annealing random-search hill-climbing meta-heuristic Machine Learning tree-of-parzen-estimator hyperparameter-optimization blackbox-optimization constrained-optimization nelder-mead hyperactive
Source Code
simonblanke.github.io
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surrogate-models

A collection of surrogate models for sequence model based optimization techniques (by SimonBlanke)
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Gradient-Free-Optimizers surrogate-models
11 1
1,108 0
- -
5.0 0.0
5 days ago about 3 years ago
Python Python
MIT License MIT License
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Gradient-Free-Optimizers

Posts with mentions or reviews of Gradient-Free-Optimizers. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-28.

surrogate-models

Posts with mentions or reviews of surrogate-models. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-28.

What are some alternatives?

When comparing Gradient-Free-Optimizers and surrogate-models you can also consider the following projects:

Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

BayesianOptimization - A Python implementation of global optimization with gaussian processes.

opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.

pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints

optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.

urh - Universal Radio Hacker: Investigate Wireless Protocols Like A Boss

prima - PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.

PSO-cont-sched - Made for a college project, this Java program attempts to demonstrate how PSO might be used to solve container scheduling problems.

Gradient-Free-Optimizers vs Hyperactive surrogate-models vs BayesianOptimization Gradient-Free-Optimizers vs opytimizer surrogate-models vs Hyperactive Gradient-Free-Optimizers vs pybobyqa surrogate-models vs optimization-tutorial Gradient-Free-Optimizers vs optimization-tutorial surrogate-models vs opytimizer Gradient-Free-Optimizers vs urh surrogate-models vs pybobyqa Gradient-Free-Optimizers vs prima Gradient-Free-Optimizers vs PSO-cont-sched