Genetic Informed Trees (GIT*): Path planning via reinforced genetic programming heuristics

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@Article{Zhang:2025:birob,

• author = "Liding Zhang and Kuanqi Cai and Zhenshan Bing and Chaoqun Wang and Alois Knoll",
• title = "Genetic Informed Trees ({GIT*):} Path planning via reinforced genetic programming heuristics",
• journal = "Biomimetic Intelligence and Robotics",
• year = "2025",
• pages = "100237",
• keywords = "genetic algorithms, genetic programming, Reinforced genetic programming, Generative heuristics, Optimal path planning",
• ISSN = "2667-3797",
• URL = "https://www.sciencedirect.com/science/article/pii/S2667379725000282",
• DOI = "doi:10.1016/j.birob.2025.100237",
• abstract = "Optimal path planning involves finding a feasible state sequence between a start and a goal that optimises an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from R4 to R16 and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at A video showcasing our experimental results is available at https://youtu.be/URjXbc_BiYg",

}

Genetic Programming entries for Liding Zhang Kuanqi Cai Zhenshan Bing Chaoqun Wang Alois Knoll

Citations