Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

[JMLR-2024] PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* versions/variants (e.g., evolutionary algorithms, swarm-based optimizers, pattern search, random search, etc.). [Citation: https://jmlr.org/papers/v25/23-0386.html (CCF-A)]

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Python library for Bayesian hyper-parameters optimization

Heuristic Optimization for Python

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Tuning the Parameters of Heuristic Optimizers (Meta-Optimization / Hyper-Parameter Optimization)

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Spark Parameter Optimization and Tuning

Feature selection package of the mlr3 ecosystem.

Different hyperparameter optimization methods to get best performance for your Machine Learning Models

Hyperparameters-Optimization

Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets

Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting.

Ithaka board game is played on a four by four square grid with three pieces in each of four colors.

Archive of my older research papers on optimization

The python implementation of Partition-based Random Search for stochastic multi-objective optimization via simulation

A simple JAX-based implementation of random search for locomotion tasks using MuJoCo XLA (MJX).