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This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.
This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.
Code for paper "Self-Directed Online Machine Learning for Topology Optimization"
An implementation of various metaheuristics adapted to train neural networks
A simple JavaScript library implementing the Bat Algorithm for optimization problems.
This is a playground for learning and working with various Optimization algorithms with a focus on real life applications.
Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm
Project to improve the efficiency of traffic signal control and reduce traffic delay at intersections by using various optimization algorithms.
UR3 Inverse Kinematics by Bat Algorithm
Iris Image Recognition Using Hybrid Backpropagation Neural Network and Bat Algorithm
Heuristic algorithms are written from scratch.
BBA-LAHC is a novel nature-inspired feature selection algorithm developed by hybridizing Binary Bat Algorithm (BBA) and Late Acceptance Hill-Climbing (LAHC) to select the optimal subset from the said feature vectors in order to reduce the model complexity.
Enhancing The Performance Of Classifiers In Detecting Abnormalities In Medical Data Using Nature Inspired Optimization Techniques
DNA fragments Assembly using multi-objective meta-heuristics.
ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction
CS F407 Project
This project aimed to implement three well-known meta-heuristic algorithms: cuckoo search (CS), bat algorithm (BA), and flower pollination algorithm (FPA). We found that three algorithms could have a promising performance generally. It might need more runs to be converged when training BA. The time cost of BA was the highest while the differences of time cost among three algorithms were not so large, which might not matter when the number of training runs was not large. We also tuned λ in CS and FPA with the findings of the weakness of larger λ values. A demo video of training process is available on YouTube: https://youtu.be/hlKvODBUyeI.