Abstract
We present an evolutionary approach to search for near-optimal solutions for the shortest path motion problem in three dimensions (between a starting and an ending point) in the presence of obstacles. The proposed genetic algorithm makes use of newly defined concepts of crossover and mutation and effective, problem optimized, methods for candidate solution generation. We test the performances of the algorithm on several test cases.
F.P. and M.P. acknowledge partial support from IMMUNOGRID project, under EC contract FP6-2004-IST-4, No. 028069.
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Pennisi, M., Pappalardo, F., Motta, A., Cincotti, A. (2007). A Genetic Algorithm for Shortest Path Motion Problem in Three Dimensions. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_58
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DOI: https://doi.org/10.1007/978-3-540-74205-0_58
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