Abstract
The concept of channel, a computational mechanism used to convey state to different threads of process execution, is at the core of the design of multi-threaded concurrent algorithms. In the case of concurrent evolutionary algorithms, channels can be used to communicate messages between several threads performing different evolution tasks related to genetic operations or mixing of populations. In this paper we study to what extent the design of these messages in a communicating sequential process context may influence scaling and performance of concurrent evolutionary algorithms. For this aim, we designed a channel-based concurrent evolutionary algorithm that is able to effectively solve different benchmark binary problems (e.g. OneMax, LeadingOnes, RoyalRoad), showing that it provides a good basis to leverage the multi-threaded and multi-core capabilities of modern computers. Although our results indicate that concurrency is advantageous to scale-up the performance of evolutionary algorithms, they also highlight how the trade–off between concurrency, communication and evolutionary parameters affect the outcome of the evolved solutions, opening-up new opportunities for algorithm design.
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This paper has been supported in part by projects DeepBio (TIN2017-85727-C4-2-P), TecNM Project 5654.19-P and CONACYT-PEI 220590.
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Merelo, J.J., Laredo, J.L.J., Castillo, P.A., García-Valdez, JM., Rojas-Galeano, S. (2019). Scaling in Concurrent Evolutionary Algorithms. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_2
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