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2024 | OriginalPaper | Buchkapitel

Evolutionary Computation Meets Stream Processing

verfasst von : Vincenzo Gulisano, Eric Medvet

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer Nature Switzerland

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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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Vorheriges Kapitel Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks
Nächstes Kapitel Integrating Data Augmentation in Evolutionary Algorithms for Feature Selection: A Preliminary Study
Literatur
1.
Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013)CrossRef
2.
3.
Carbone, P., Fragkoulis, M., Kalavri, V., Katsifodimos, A.: Beyond analytics: the evolution of stream processing systems. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2651–2658 (2020)
4.
Cardellini, V., Lo Presti, F., Nardelli, M., Russo, G.R.: Runtime adaptation of data stream processing systems: the state of the art. ACM Comput. Surv. 54(11s), 1–36 (2022)CrossRef
5.
De Lorenzo, A., Bartoli, A., Castelli, M., Medvet, E., Xue, B.: Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence. Genet. Program Evolvable Mach. 21, 181–204 (2020)CrossRef
6.
Duvignau, R., Gulisano, V., Papatriantafilou, M., Savic, V.: Streaming piecewise linear approximation for efficient data management in edge computing. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (2019)
7.
8.
9.
Fortin, F.A., De Rainville, F.M., Gardner, M.A.G., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(1), 2171–2175 (2012)MathSciNet
10.
Frasca, F., Gulisano, V., Mencagli, G., Palyvos-Giannas, D., Torquati, M.: Accelerating stream processing queries with congestion-aware scheduling and real-time Linux threads. In: Proceedings of the 20th ACM International Conference on Computing Frontiers, pp. 144–153 (2023)
11.
Gulisano, V., Jimenez-Peris, R., Patino-Martinez, M., Soriente, C., Valduriez, P.: StreamCloud: an elastic and scalable data streaming system. IEEE Trans. Parallel Distrib. Syst. 23(12), 2351–2365 (2012)CrossRef
12.
Gulisano, V., Palyvos-Giannas, D., Havers, B., Papatriantafilou, M.: The role of event-time order in data streaming analysis. In: Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems, DEBS 2020, pp. 214–217. Association for Computing Machinery, New York (2020). ISBN 9781450380287. https://​doi.​org/​10.​1145/​3401025.​3404088
13.
Gulisano, V., Papadopoulos, A.V., Nikolakopoulos, Y., Papatriantafilou, M., Tsigas, P.: Performance modeling of stream joins. In: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, pp. 191–202 (2017)
14.
Harada, T., Alba, E.: Parallel genetic algorithms: a useful survey. ACM Comput. Surv. 53(4), 1–39 (2020)CrossRef
15.
Havers, B., Duvignau, R., Najdataei, H., Gulisano, V., Koppisetty, A.C., Papatriantafilou, M.: DRIVEN: a framework for efficient data retrieval and clustering in vehicular networks. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1850–1861. IEEE (2019)
16.
Hummer, W., Satzger, B., Dustdar, S.: Elastic stream processing in the cloud. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 3(5), 333–345 (2013)CrossRef
17.
Isah, H., Abughofa, T., Mahfuz, S., Ajerla, D., Zulkernine, F., Khan, S.: A survey of distributed data stream processing frameworks. IEEE Access 7, 154300–154316 (2019)CrossRef
18.
La Cava, W., et al.: Contemporary symbolic regression methods and their relative performance. arXiv preprint arXiv:​2107.​14351 (2021)
19.
Maitre, O., Baumes, L.A., Lachiche, N., Corma, A., Collet, P.: Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1403–1410 (2009)
20.
21.
Medvet, E., Nadizar, G., Manzoni, L.: JGEA: a modular java framework for experimenting with evolutionary computation. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2009–2018 (2022)
22.
Najdataei, H., Gulisano, V., Tsigas, P., Papatriantafilou, M.: pi-Lisco: parallel and incremental stream-based point-cloud clustering. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 460–469 (2022)
23.
Najdataei, H., Nikolakopoulos, Y., Gulisano, V., Papatriantafilou, M.: Continuous and parallel LiDAR point-cloud clustering. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 671–684. IEEE (2018)
24.
Palyvos-Giannas, D., Havers, B., Papatriantafilou, M., Gulisano, V.: Ananke: a streaming framework for live forward provenance. Proc. VLDB Endow. 14(3), 391–403 (2020)CrossRef
25.
Palyvos-Giannas, D., Mencagli, G., Papatriantafilou, M., Gulisano, V.: Lachesis: a middleware for customizing OS scheduling of stream processing queries. In: Proceedings of the 22nd International Middleware Conference, pp. 365–378 (2021)
26.
Palyvos-Giannas, D., Tzompanaki, K., Papatriantafilou, M., Gulisano, V.: Erebus: explaining the outputs of data streaming queries. In: Very Large Data Base, vol. 16, pp. 230–242 (2023)
27.
Pigozzi, F., Medvet, E.: Evolving modularity in soft robots through an embodied and self-organizing neural controller. Artif. Life 28(3), 322–347 (2022)CrossRef
28.
29.
30.
Röger, H., Mayer, R.: A comprehensive survey on parallelization and elasticity in stream processing. ACM Compu. Surv. (CSUR) 52(2), 1–37 (2019)CrossRef
31.
Rovito, L., De Lorenzo, A., Manzoni, L.: Evolution of Walsh Transforms with genetic programming. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 2386–2389 (2023)
32.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)CrossRef
33.
Russo, G.R., Cardellini, V., Presti, F.L.: Reinforcement learning based policies for elastic stream processing on heterogeneous resources. In: Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems, pp. 31–42 (2019)
34.
35.
Virgolin, M., Alderliesten, T., Bosman, P.A.: Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1084–1092 (2019)
36.
White, D.R., et al.: Better GP benchmarks: community survey results and proposals. Genet. Program Evolvable Mach. 14, 3–29 (2013)CrossRef
Metadaten
Titel
Evolutionary Computation Meets Stream Processing
verfasst von
Vincenzo Gulisano
Eric Medvet
Copyright-Jahr
2024
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-031-56851-0

Electronic ISBN: 978-3-031-56852-7

Copyright-Jahr: 2024

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

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