An improved genetic algorithm for causal discovery

doi:10.23919/JSEE.2025.000015

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 768-777.doi: 10.23919/JSEE.2025.000015

• SYSTEMS ENGINEERING • Previous Articles

An improved genetic algorithm for causal discovery

Tengjiao MAO(), Xianjin BU(), Chunxiao CAI, Yue LU(), Jing DU()

Evaluation Research Department of Center for Strategic Assessment and Consulting, Academy of Military Sciences, Beijing 100097, China

Received:2023-09-13 Online:2025-06-18 Published:2025-07-10
Contact: Xianjin BU E-mail:maotengjiao_ams@126.com;ytbxj@163.com;Olivia9608@hotmail.com;jdstarry@aliyun.com
About author:
MAO Tengjiao was born in 1992. He received his M.S degree in operation research from Army Engineering University of PLA in 2021. He is currently pursuing his Ph.D. degree in military operation research at Academy of Military Science. His research interests include causal inference, military assessment, and operation research. E-mail: maotengjiao_ams@126.com

BU Xianjin was born in 1964. He received his Ph.D. degree from National University of Defense Technology of PLA in 2009. He is currently a researcher in the Center for Strategic Assessment and Consulting, Academy of Military Science. His research interests include causal inference, military assessment, and operation research. E-mail: ytbxj@163.com

CAI Chunxiao was born in 1985. He received his Ph.D. degree from Army Engineering University of PLA in 2012. He is currently a researcher in Center for Strategic Assessment and Consulting, Academy of Military Science. His research interests include operation research and systems engineering. E-mail: caichunxiao1007 @163.com

LU Yue was born in 1996. She received her M.S. degree in computer science from University College Dublin in 2019. She is currently a researcher assistant in the Center for Strategic Assessment and Consulting, Academy of Military Science. Her research interests include military assessment and causal inference. E-mail: Olivia9608@hotmail.com

DU Jing was born in 1979. She received his Ph.D. degree from Army Engineering University of PLA. She is currently a researcher in the Center for Strategic Assessment and Consulting, Academy of Military Science. Her research interests include evaluation theory method and intelligent testing. E-mail: jdstarry@aliyun.com
Supported by:
This work was supported by the National Social Science Fund of China (2022-SKJJ-B-084).

Abstract

Abstract:

The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms (GA). The score-based algorithms are prone to searching space explosion. Classical GA is slow to converge, and prone to falling into local optima. To address these issues, an improved GA with domain knowledge (IGADK) is proposed. Firstly, domain knowledge is incorporated into the learning process of causality to construct a new fitness function. Secondly, a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate. Finally, an experiment is conducted on simulation data, which compares the classical GA with IGADK with domain knowledge of varying accuracy. The IGADK can greatly reduce the number of iterations, populations, and samples required for learning, which illustrates the efficiency and effectiveness of the proposed algorithm.

Key words: genetic algorithm (GA), causal discovery, convergence rate, fitness function, mutation operator

Tengjiao MAO, Xianjin BU, Chunxiao CAI, Yue LU, Jing DU. An improved genetic algorithm for causal discovery[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 768-777.

Figures/Tables 12

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Scenario	Sample size
Scenario 1	720
Scenario 2	100
Scenario 3	450
Scenario 4	720

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An improved genetic algorithm for causal discovery

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Algorithm	Iteration number	Population size
GA	41	No convergence
IGADK	19	24