Search Results for "muti-objective optimization"

BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes.

A script(ing/ed/able) optimizing compiler for the C and Objective-C languages (and in the future other languages such as C++, D and Java). Written in a scripting language to allow for experimentation with code generation and optimization techniques.

PyOptFrame-LEGACY is a Python version of OptFrame v2, proposed in 2011, now superseeded in 2021 by v5 on GitHub and PIP. The main objective is to provide the same interface to OptFrame C++ optimization framework, including classic metaheuristics such as genetic algorithms, simulated annealing, variable neighborhood search, first/best/multi-improvement, hill climbing, and multi-objective methods such as nsga-ii. See NEWEST version v5 on GitHub and PIP. Please try Official pyoptframe on https://pypi.org/project/optframe/ for OptFrame v5 (last updated 2022).