Study on Evolutionary Algorithm Online Performance Evaluation Visualization Based on Python Programming Language

Shi Ruifeng (), Zhang Ning (), Jiao Runhai (), Zhou Zhenyu () and Zhang Li ()
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Shi Ruifeng: School of Control & Computer Engineering, North China Electric Power University, Beijing102206, China
Zhang Ning: School of Control & Computer Engineering, North China Electric Power University, Beijing102206, China
Jiao Runhai: School of Control & Computer Engineering, North China Electric Power University, Beijing102206, China
Zhou Zhenyu: School of Electric & Electronics Engineering, North China Electric Power University, Beijing102206, China
Zhang Li: High-Tech Research and Development Center, Ministry of Science and Technology, Beijing100044, China

Journal of Systems Science and Information, 2014, vol. 2, issue 1, 86-96

Abstract: Evolutionary computations are kinds of random searching algorithms derived from natural selection and biological genetic evolution behavior. Evaluating the performance of an algorithm is a fundamental task to track and find the way to improve the algorithm, while visualization technique may play an important act during the process. Based on current existing algorithm performance evaluation criteria and methods, a Python-based programming tracking strategy, which employs 2-D graphical library of python matplotlib for online algorithm performance evaluation, is proposed in this paper. Tracking and displaying the performance of genetic algorithm (GA) and particle swarm optimization (PSO) optimizing two typical numerical benchmark problems are employed for verification and validation. Results show that the tracking strategy based on Python language for online performance evaluation of evolutionary algorithms is valid, and can be used to help researchers on algorithms’ performance evaluation and finding ways to improve it.

Keywords: evolutionary algorithm; online performance evaluation; Python language; genetic algorithm; particle swarm optimization (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:2:y:2014:i:1:p:86-96:n:8

DOI: 10.1515/JSSI-2014-0086

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