Adaptive Memetic Binary Optimization (AMBO) Algorithm

版本 1.0.0 (147.7 KB) 作者: Ahmet Cevahir ÇINAR
A novel adaptive memetic binary optimization algorithm for feature selection
0.0
(0)
26.0 次下载
更新时间 2025/7/25
在 GitHub 上查看许可证
AMBO: Adaptive Memetic Binary Optimization Algorithm for Feature Selection
This repository contains the official MATLAB implementation of the AMBO (Adaptive Memetic Binary Optimization) algorithm proposed in the paper:
A. C. Çınar, A novel adaptive memetic binary optimization algorithm for feature selection, Artificial Intelligence Review, 2023. DOI: 10.1007/s10462-023-10482-8
📌 About the Project
AMBO is a pure binary metaheuristic algorithm specifically designed for feature selection tasks. It uses:
  • Adaptive crossover mechanisms (single-point, double-point, uniform)
  • Canonical mutation
  • Logic gate-based local search using AND, OR, and XOR for balancing exploration and exploitation.
It has been tested on 21 benchmark datasets and outperformed several state-of-the-art algorithms including BPSO, GA variants, BDA, BSSA, and BGWO.
📂 Files
  • Main.m: Main script to run the algorithm.
  • datasets/: Sample datasets used in the paper.
  • results/: Contains output logs and performance results.
🧪 Requirements
  • MATLAB R2021a or later
  • Statistics and Machine Learning Toolbox (for KNN)
📈 Citation
If you use this code or data in your research, please cite the paper as:
@article{cinar2023ambo,
title={A novel adaptive memetic binary optimization algorithm for feature selection},
author={Cinar, Ahmet Cevahir},
journal={Artificial Intelligence Review},
year={2023},
doi={10.1007/s10462-023-10482-8}
}
🤝 Collaboration
Contributions, ideas, and collaborations are welcome!
Feel free to contact me for research partnerships, extensions, or comparative benchmarking:
📧 Email: ahmetcevahircinar@gmail.com
🔗 LinkedIn: Ahmet Cevahir Çınar

引用格式

@article{cinar2023ambo, title={A novel adaptive memetic binary optimization algorithm for feature selection}, author={Cinar, Ahmet Cevahir}, journal={Artificial Intelligence Review}, year={2023}, doi={10.1007/s10462-023-10482-8} }

MATLAB 版本兼容性
创建方式 R2025a
兼容任何版本
平台兼容性
Windows macOS Linux
标签 添加标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

无法下载基于 GitHub 默认分支的版本

版本 已发布 发行说明
1.0.0

要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 仓库。