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A Protocol for Computer-Based Protein Structure and Function Prediction
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Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure

Xiao-Gen Zhou1, Chun-Xiang Peng2, Jun Liu2

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China, and also with the Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

IEEE Transactions on Evolutionary Computation : a Publication of the IEEE Neural Networks Council
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a cooperative differential evolution (DE) algorithm that combines global exploration and local exploitation phases. This enhanced DE approach improves efficiency and effectiveness in optimization tasks, including protein 3D structure prediction.

Keywords:
Differential evolutioncooperationevolutionary algorithmprotein structure predictionunderestimation

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Area of Science:

  • Computational Biology
  • Optimization Algorithms
  • Bioinformatics

Background:

  • Differential evolution (DE) algorithms utilize various mutation strategies for optimization.
  • Cooperation between multiple DE strategies can potentially enhance performance.
  • Existing DE methods may lack simultaneous effectiveness and efficiency in complex problems.

Purpose of the Study:

  • To propose an underestimation-assisted global and local cooperative DE algorithm.
  • To enhance both the effectiveness and efficiency of differential evolution.
  • To improve performance in complex optimization tasks like protein structure prediction.

Main Methods:

  • Implementing a two-phase approach (global exploration and local exploitation) within each generation.
  • Utilizing multiple DE strategies for generating trial vectors in the global phase.
  • Employing an adaptive underestimation model with self-adapted slope control for trial vector evaluation.
  • Designing better-based strategies for the local exploitation phase.
  • Incorporating adaptive parameter control for DE.

Main Results:

  • The proposed cooperative DE algorithm demonstrates superior performance compared to competitors.
  • Experimental results on benchmark functions and CEC test sets validate the approach.
  • The algorithm shows significant effectiveness and efficiency in protein 3D structure prediction.

Conclusions:

  • The underestimation-assisted global and local cooperative DE effectively balances exploration and exploitation.
  • The cooperative strategy enhances the overall performance of differential evolution.
  • The proposed method offers a robust solution for complex optimization problems, particularly in bioinformatics.