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Parallel genetic programming: Synchronous and asynchronous migration

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Abstract

Genetic programming (GP) has been used successfully as a technique for constructing robot control programs. Depending on the number of evaluations and the cost of each evaluation however, GP may require a substantial amount of processing time to find a feasible solution. The advent of parallel GP has brought the execution time of GP to a more acceptable level. This paper investigates parallel GP with a mobile robot navigation problem. The parallel implementations are based on a coarse-grained model. A technique for distributing the task of serial GP is proposed. In particular, this technique shows that the total amount of work can be reduced while maintaining the quality of the solutions. Asynchronous and synchronous implementations are examined. We compare the performance in terms of both the solution quality and the execution time. The timing analysis is investigated to give an insight into the behavior of parallel implementations. The results show that the parallel algorithm with asynchronous migration using 10 processors is 33 times faster than the serial algorithm.

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Correspondence to Shisanu Tongchim.

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Tongchim, S., Chongstitvatana, P. Parallel genetic programming: Synchronous and asynchronous migration. Artif Life Robotics 5, 189–194 (2001). https://doi.org/10.1007/BF02481500

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  • DOI: https://doi.org/10.1007/BF02481500

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