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Evolutionary Computation Meets Stream Processing

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Applications of Evolutionary Computation (EvoApplications 2024)

Abstract

Evolutionary computation (EC) has a great potential of exploiting parallelization, a feature often underemphasized when describing evolutionary algorithms (EAs). In this paper, we show that the paradigm of stream processing (SP) can be used to express EAs in a way that allows the immediate exploitation of parallel and distributed computing, not at the expense of the agnosticity of the EAs with respect to the application domain. We introduce the first formal framework for EC based on SP and describe several building blocks tailored to EC. Then, we experimentally validate our framework and show that (a) it can be used to express common EAs, (b) it scales when deployed on real-world stream processing engines (SPEs), and (c) it facilitates the design of EA modifications which would require a larger effort with traditional implementation.

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Acknowledgements

This study was carried out within the PNRR research activities of the consortium iNEST (Interconnected North-Est Innovation Ecosystem) funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa e Resilienza (PNRR) - Missione 4 Componente 2, Investimento 1.5 - D.D. 1058 23/06/2022, ECS_00000043), and by the Marie Skłodowska-Curie Doctoral Network project RELAX-DN, funded by the European Union under Horizon Europe 2021–2027 Framework Programme Grant Agreement number 101072456.

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Gulisano, V., Medvet, E. (2024). Evolutionary Computation Meets Stream Processing. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14634. Springer, Cham. https://doi.org/10.1007/978-3-031-56852-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-56852-7_24

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