Cross-Representation Genetic Programming: A Case Study on Tree-Based and Linear Representations

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@Article{Huang:2025:EC,

• author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang and Wolfgang Banzhaf",
• title = "Cross-Representation Genetic Programming: A Case Study on Tree-Based and Linear Representations",
• journal = "Evolutionary Computation",
• year = "2025",
• volume = "33",
• number = "4",
• pages = "541--568",
• month = "Winter",
• keywords = "genetic algorithms, genetic programming, Cross-representation, tree-based genetic programming, linear genetic programming, symbolic regression, dynamic job shop scheduling",
• ISSN = "1063-6560",
• eprint = "https://direct.mit.edu/evco/article-pdf/33/4/541/2523398/evco.a.25.pdf",
• DOI = "10.1162/evco.a.25",
• abstract = "... We proposes a cross-representation GP algorithm based on tree-based and linear representations, which are two commonly used GP representations. In addition, we develop a new cross-representation crossover operator ... Empirical results show that navigating the learned knowledge between basic tree-based and linear representations successfully improves the effectiveness of GP with solely tree-based or linear representation in solving symbolic regression and dynamic job shop scheduling problems.",
• notes = "Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand",

}

Genetic Programming entries for Zhixing Huang Yi Mei Fangfang Zhang Mengjie Zhang Wolfgang Banzhaf

Citations