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Related Experiment Video

Updated: Aug 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem.

Jeng-Shyang Pan^1,2, Longkang Yue¹, Shu-Chuan Chu¹

¹College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Entropy (Basel, Switzerland)

|February 25, 2023

Summary

This summary is machine-generated.

This study introduces the Binary Bamboo Forest Growth Optimization (BBFGO) algorithm for binary optimization problems. BBFGO enhances convergence speed and performance, demonstrating effectiveness in feature selection for classification tasks.

Keywords:

bamboo forest growth optimization binary feature selection optimization transfer function

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

The standard Bamboo Forest Growth Optimization (BFGO) algorithm is effective for continuous optimization but not directly applicable to binary problems.
Binary optimization problems require specialized algorithms due to the discrete nature of decision variables (0 or 1).

Purpose of the Study:

To propose a binary version of the BFGO algorithm, termed Binary BFGO (BBFGO), for addressing binary optimization challenges.
To introduce novel V-shaped and Taper-shaped transfer functions for converting continuous values to binary within the BBFGO framework.
To develop a long-mutation strategy to mitigate algorithmic stagnation in BBFGO.

Main Methods:

Development of the Binary BFGO (BBFGO) algorithm, adapting the BFGO principles for binary search spaces.
Introduction of new V-shaped and Taper-shaped transfer functions to map continuous search space values to binary outputs.
Implementation of a long-mutation strategy with a novel mutation approach to enhance exploration and escape local optima.
Empirical evaluation on 23 benchmark test functions and 12 UCI machine learning repository datasets for feature selection.

Main Results:

BBFGO demonstrated superior performance in finding optimal values and achieving faster convergence compared to existing binary optimization methods on benchmark functions.
The proposed long-mutation strategy significantly improved the overall performance and robustness of the BBFGO algorithm.
BBFGO showed strong potential in feature selection tasks, effectively identifying significant features for classification problems.

Conclusions:

The developed BBFGO algorithm is a viable and effective approach for solving binary optimization problems.
The novel transfer functions and mutation strategy contribute to enhanced optimization capabilities.
BBFGO shows promise for practical applications, particularly in feature selection for machine learning.