Genetic Programming to Optimize 3D Trajectories

Created by W.Langdon from gp-bibliography.bib Revision:1.8528

@Article{kotze:2024:ISPRS,

• author = "Andre Kotze and Moritz Jan Hildemann and Vitor Santos and Carlos Granell",
• title = "Genetic Programming to Optimize {3D} Trajectories",
• journal = "ISPRS International Journal of Geo-Information",
• year = "2024",
• volume = "13",
• number = "8",
• pages = "Article No. 295",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2220-9964",
• URL = "https://www.mdpi.com/2220-9964/13/8/295",
• DOI = "doi:10.3390/ijgi13080295",
• abstract = "Trajectory optimisation is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimise the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimisation with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimisation by developing a novel GP algorithm to optimise trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimisation problems in science and engineering.",
• notes = "also known as \cite{ijgi13080295}",

}

Genetic Programming entries for Andre Kotze Moritz Jan Hildemann Vitor Santos Carlos Granell

Citations