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A modern, event-based architecture for distributed evolutionary algorithms

Published: 06 July 2018 Publication History

Abstract

In this paper we introduce KafkEO, a cloud native evolutionary algorithms framework that is prepared to work with population-based metaheuristics by using micro-populations and stateless services as the main building blocks; KafkEO is an attempt to map the traditional evolutionary algorithm to this new cloud-native format.

References

[1]
A. Bollini and M. Piastra. 1999. Distributed and persistent evolutionary algorithms: a design pattern. In Genetic Programming, Proceedings EuroGP'99 (Lecture notes in computer science). Springer, 173--183.
[2]
Andrea De Lucia and Pasquale Salza. 2017. Parallel Genetic Algorithms in the Cloud. (2017), 1--166.
[3]
Gilberto Viana de Oliveira and Murilo Coelho Naldi. 2015. Scalable Fast Evolutionary k-Means Clustering. In Intelligent Systems (BRACIS), 2015 Brazilian Conference on. IEEE, 74--79.
[4]
Włodzimierz Funika and Paweł Koperek. 2016. Towards a Scalable Distributed Fitness Evaluation Service. In Parallel Processing and Applied Mathematics, Roman Wyrzykowski, Ewa Deelman, Jack Dongarra, Konrad Karczewski, Jacek Kitowski, and Kazimierz Wiatr (Eds.). Springer International Publishing, Cham, 493--502.
[5]
Mario García-Valdez, Leonardo Trujillo, Juan-J Merelo, Francisco Fernández de Vega, and Gustavo Olague. 2015. The EvoSpace Model for Pool-Based Evolutionary Algorithms. Journal of Grid Computing 13, 3 (01 Sep 2015), 329--349.
[6]
Pasquale Salza, Erik Hemberg, Filomena Ferrucci, and Una-May O'Reilly. 2017. cCube: a cloud microservices architecture for evolutionary machine learning classification. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, 137--138.

Cited By

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  • (2022)Mixing Population-Based Metaheuristics: An Approach Based on a Distributed-Queue for the Optimal Design of Fuzzy ControllersIntelligent and Fuzzy Systems10.1007/978-3-031-09173-5_96(839-846)Online publication date: 5-Jul-2022
  • (2022)Optimization of Fuzzy-Control Parameters for Path Tracking of a Mobile Robot Using Distributed Genetic AlgorithmsNew Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics10.1007/978-3-031-08266-5_12(167-177)Online publication date: 1-Oct-2022
  • (2021)Can Communication Topology Improve a Multi-swarm PSO Algorithms?Applied Computer Sciences in Engineering10.1007/978-3-030-86702-7_1(3-12)Online publication date: 29-Sep-2021
  • Show More Cited By

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

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Author Tags

  1. cloud computing
  2. distributed computing
  3. event-based systems
  4. functions as a service
  5. microservices
  6. stateless algorithms

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2022)Mixing Population-Based Metaheuristics: An Approach Based on a Distributed-Queue for the Optimal Design of Fuzzy ControllersIntelligent and Fuzzy Systems10.1007/978-3-031-09173-5_96(839-846)Online publication date: 5-Jul-2022
  • (2022)Optimization of Fuzzy-Control Parameters for Path Tracking of a Mobile Robot Using Distributed Genetic AlgorithmsNew Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics10.1007/978-3-031-08266-5_12(167-177)Online publication date: 1-Oct-2022
  • (2021)Can Communication Topology Improve a Multi-swarm PSO Algorithms?Applied Computer Sciences in Engineering10.1007/978-3-030-86702-7_1(3-12)Online publication date: 29-Sep-2021
  • (2021)Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary StrategiesApplications of Evolutionary Computation10.1007/978-3-030-72699-7_47(747-762)Online publication date: 1-Apr-2021
  • (2019)Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factoriesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321826(1381-1389)Online publication date: 13-Jul-2019
  • (2019)Concurrency in evolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323395(1172-1174)Online publication date: 13-Jul-2019
  • (2019)Cloud-Based Integrated Process Planning and Scheduling Optimisation via Asynchronous IslandsEconomics of Grids, Clouds, Systems, and Services10.1007/978-3-030-36027-6_21(247-259)Online publication date: 24-Nov-2019
  • (2019)Scaling in Concurrent Evolutionary AlgorithmsApplied Computer Sciences in Engineering10.1007/978-3-030-31019-6_2(16-27)Online publication date: 9-Oct-2019
  • (2019)Exploring Concurrent and Stateless Evolutionary AlgorithmsTheory and Applications of Models of Computation10.1007/978-3-030-16692-2_27(405-412)Online publication date: 30-Mar-2019

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