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Related Experiment Video

Updated: Aug 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem.

Jeng-Shyang Pan1,2, 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
PubMed
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 optimizationbinaryfeature selectionoptimizationtransfer 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.