Abstract
Unlike a synchronous program evolution in the context of evolutionary computation that evolves individuals (i.e., programs) after evaluations of all individuals in each generation, this paper focuses on an asynchronous program evolution that evolves individuals during evaluations of each individual. To tackle this problem, we explore the mechanism that can promote an asynchronous program evolution by selecting a good individual without waiting for evaluations of all individuals, and investigates its effectiveness in genetic programming (GP) domain. The intensive experiments have revealed the following implications: (1) the program asynchronously evolved with the proposed mechanism can be completed with the shorter execution steps than the program asynchronously evolved without the proposed mechanism; and (2) the program asynchronously evolved with the proposed mechanism can be completed with mostly the same or shorter execution steps than the program synchronously evolved by the conventional GP.
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Takadama, K., Harada, T., Sato, H., Hattori, K. (2014). What is Needed to Promote an Asynchronous Program Evolution in Genetic Programing?. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_22
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DOI: https://doi.org/10.1007/978-3-319-09584-4_22
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