Different meta-heuristic optimization techniques for feature selection
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Updated
May 19, 2020 - Python
Different meta-heuristic optimization techniques for feature selection
Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators ser…
Metaheuristic Minimization Using Particle Swarm Optimization.
Adaptive Multi-Population Optimization Algorithm
reflame: Revolutionizing Functional Link Neural Network by Metaheuristic Optimization
MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library
The traveling salesman problem (TSP) is a well-known problem in theoretical computer science and operations research. The standard version of the TSP is a hard problem and belongs to the NP-Hard class. In this project, I build an application to implement the TSP by the dynamic approach and the GVNS approach .
The codes for metaheuristic optimization algorithms
EvoRBF: Evolving Radial Basis Function Network by Intelligent Nature-inspired Algorithms
This is a project of portfolio optimization using Quantum-inspired Tabu Search and Trend Ratio
Enhancing the performance of high dimensional automatic data clustering using Particle Swarm Optimization (PSO) algorithm employing Autoencoder in Stock Market data.
Demonstration of Particle Swarm Optimization (Auto Hyperparameter variant).
(ancient german = improving, rearranging, rendering benign)
Final project for Methaeuristics subject, taught at the University of Granada during the 2019/2020 academic year.
A toolkit for MetaHeuristics implemented in Python
Algorithmes de selection de variables pour préparer un apprentissage non supervisé. La version finale du programme est sélectionne les prédicteurs les plus pertinents en effectuant un apprentissage à chaque génération. La métrique optimisée (dans le cadre du dataset utilisé) est l'accuracy. Nous avons testé les deux métaheuristiques sur un datas…
A project for Fundamental of Optimization class at HUST, Winter 2022
Implementation of some metaheuristic algorithms.
This work was aimed at finding methods to identify the most distant proteins and most diverse subsets of proteins from large protein databases in a scalable and efficient way using a dataset of protein embeddings from SwissProt, data mining techniques and metaheuristics.
(Code) A Novel Nature-inspired Algorithm for Optimal Task Scheduling in Fog-Cloud Blockchain System
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