A framework for single/multi-objective optimization with metaheuristics
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Updated
May 29, 2024 - Python
A framework for single/multi-objective optimization with metaheuristics
OptFrame - C++17 (and C++20) Optimization Framework in Single or Multi-Objective. Supports classic metaheuristics and hyperheuristics: Genetic Algorithm, Simulated Annealing, Tabu Search, Iterated Local Search, Variable Neighborhood Search, NSGA-II, Genetic Programming etc. Examples for Traveling Salesman, Vehicle Routing, Knapsack Problem, etc.
Python bindings for OptFrame C++ Functional Core
A Memetic Procedure for Global Multi-Objective Optimization
An R package for multi/many-objective optimization with non-dominated genetic algorithms' family
Genetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)
(Code) Multi-objective Sparrow Search Optimization for Task Scheduling in Fog-Cloud-Blockchain Systems
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Code for MultiObjectiveOptimizationClass
pfevaluator: A library for evaluating performance metrics of Pareto fronts in multiple/many objective optimization problems
A Framework for High-dimensional Pareto-optimal Front Visualization and Analytics
Bayesian Multi-Objective Optimization
Implementation of NSGA-II in Python
Evolutionary & genetic algorithms for Julia
Official repository of "Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models" [ICML 2023]
📝 Implementation of our approach for balancing the utility of the decision maker and the fairness towards the decision subjects for a prediction-based decision-making system
Using Differential Evolution with the NSGA II algorithm to solve multi-objective optimization problems
Example of Pareto Front Calculation Using KD-Tree With C++
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