José-Mario García-Valdez
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.
- 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. Google Scholar
Digital Library
- Andrea De Lucia and Pasquale Salza. 2017. Parallel Genetic Algorithms in the Cloud. (2017), 1--166.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- A modern, event-based architecture for distributed evolutionary algorithms
Recommendations
Mapping evolutionary algorithms to a reactive, stateless architecture: using a modern concurrent language
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference CompanionGenetic algorithms (GA) [8] are currently one of the most widely used meta-heuristics to solve engineering problems. Furthermore, parallel genetic algorithms (pGAs) are useful to find solutions of complex optimizations problems in adequate times [16]; ...
Monitoring-based auto-scalability across hybrid clouds
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingCloud computing is a relatively new type of Internet-based computing that becomes more and more popular. Using methods like virtualization, adopting architectures based on microservices, automation of building and deployment processes, Cloud could ...
Towards evolutionary machine learning comparison, competition, and collaboration with a multi-cloud platform
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference CompanionWe present cCube, an open source architecture used to automatically create an application of one or more Evolutionary Machine Learning (EML) classification algorithms that can be deployed to the cloud with automatic data factorization, training, result ...
Comments