oacde 1.0.2

Online algorithm configuration for differential evolution algorithm (OAC-DE)

Online algorithm configuration for differential evolution algorithm (OAC-DE)

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Abstract

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance.

Keywords

Automatic algorithm configuration, Differential evolution algorithm, Adaptive parameter control, Multi-armed bandit, Kernel density estimation, Machine learning

About this repository

How to install

pip install oacde

How to use

import oacde

Tutorial

Click here to read the full tutorial.

Citation

1. You may cite this work in a scientific context as:

   C. Huang, H. Bai and X. Yao, "Online algorithm configuration for differential evolution algorithm", Applied Intelligence, 2022, vol. 52, no. 8, pp. 9193–9211, doi: 10.1007/s10489-021-02752-1

2. Or copy the folloing BibTex file:

   @article{OACDE,
   author  = {Changwu Huang and Hao Bai and Xin Yao},
   title   = {Online algorithm configuration for differential evolution algorithm},
   journal = {Applied Intelligence},
   volume  = {52},
   number  = {8},
   pages   = {9193-9211},
   year    = {2022},
   url     = {https://link.springer.com/article/10.1007/s10489-021-02752-1},
   doi     = {10.1007/s10489-021-02752-1},
   }
   

3. Or download the citation in RIS file.

Related work

Adaptive Differential Evolution based on Exploration and Exploitation Control (AEEC-DE)

Contact

Asst. Prof. Changwu HUANG