Island model in ActoDatA: an actor-based implementation of a classical distributed evolutionary computation paradigm
Authors: 
Giuseppe Petrosino, Federico Bergenti, Gianfranco Lombardo, Monica Mordonini, Agostino Poggi, Michele Tomaiuolo, Stefano CagnoniAuthors Info & Claims
Giuseppe Petrosino, Federico Bergenti, Gianfranco Lombardo, Monica Mordonini, Agostino Poggi, Michele Tomaiuolo, Stefano CagnoniAuthors Info & ClaimsPages 1801 - 1808
Abstract
In this paper, we make a first assessment of the performance of ActoDatA, a novel actor-based software library for distributed data analysis and machine learning in Java that we have recently developed. To do so we have implemented an evolutionary machine learning application based on a distributed island model. The model implementation is compared to an equivalent implementation in ECJ, a popular general-purpose evolutionary computation library that provides support for distributed computing.
The testbed used for comparing the two distributed versions has been an application of Sub-machine code Genetic Programming to the design of efficient low-resolution binary image classifiers. The results we have obtained show that the ActoDatA implementation is more efficient than the corresponding ECJ implementation.
References
[1]
Gul Agha. 1990. Concurrent object-oriented programming. Commun. ACM 33, 9 (1990).
[2]
Giulio Angiani, Paolo Fornacciari, Gianfranco Lombardo, Agostino Poggi, and Michele Tomaiuolo. 2018. Actors based agent modelling and simulation. In Communications in Computer and Information Science, Vol. 887.
[3]
Federico Bergenti, Eleonora Iotti, Agostino Poggi, and Michele Tomaiuolo. 2016. Concurrent and Distributed Applications with ActoDeS. In MATEC Web of Conferences, Vol. 76.
[4]
Stefano Cagnoni, Federico Bergenti, Monica Mordonini, and Giovanni Adorni. 2005. Evolving binary classifiers through parallel computation of multiple fitness cases. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35, 3 (2005), 548--555.
[5]
Vinícius Veloso de Melo, Álvaro Luiz Fazenda, Léo Françoso Dal Piccol Sotto, and Giovanni Iacca. 2020. A MIMD Interpreter for Genetic Programming. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, 645--658.
[6]
A. E. Eiben and James E. Smith. 2015. Introduction to Evolutionary Computing (2nd ed.). Springer Publishing Company, Incorporated.
[7]
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research 13, 1 (2012), 2171--2175.
[8]
Carl Hewitt, Peter Bishop, and Richard Steiger. 1973. A Universal Modular ACTOR Formalism for Artificial Intelligence. Advance Papers of the Conference (1973).
[9]
JetBrains. [n.d.]. KotlinDL: High-level Deep Learning API in Kotlin. https://github.com/jetbrains/kotlindl retrieved on Jan 30th, 2021.
[10]
JetBrains and Kotlin Community. [n.d.]. Kotlin-jupiter: a Kotlin kernel for Jupiter. https://github.com/Kotlin/kotlin-jupyter retrieved on Jan 30th, 2021.
[11]
Guillaume Leclerc, Joshua E. Auerbach, Giovanni Iacca, and Dario Floreano. 2016. The Seamless Peer and Cloud Evolution Framework. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (Denver, Colorado, USA) (GECCO '16). Association for Computing Machinery, New York, NY, USA, 821--828.
[12]
Lightbend. [n.d.]. Akka Platform by Lightbend. https//www.lightbend.com/akka-platform
[13]
Gianfranco Lombardo, Paolo Fornacciari, Monica Mordonini, Michele Tomaiuolo, and Agostino Poggi. 2019. A multi-agent architecture for data analysis. Future Internet 11, 2 (2019).
[14]
Gianfranco Lombardo and Agostino Poggi. 2020. A preliminary experimentation for large scale epidemic forecasting simulations. In CEUR Workshop Proceedings, Vol. 2706.
[15]
Sean Luke. 2010. The ECJ Owner's Manual.
[16]
Sean Luke, Eric O. Scott, Liviu Pnait, Gabriel Balan, et al. [n.d.]. ECJ 27: A Java-based Evolutionary Computation Research System. https://cs.gmu.edu/~eclab/projects/ecj/
[17]
Luca Mussi, Fabio Daolio, and Stefano Cagnoni. 2011. Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Information Sciences 181, 20 (2011), 4642--4657.
[18]
Riccardo Poli, William B Langdon, Nicholas F McPhee, and John R Koza. 2008. A field guide to genetic programming. Lulu. com.
[19]
Ievgen Sidenko, Galyna Kondratenko, and Valentyn Petrovych. 2020. Neural network technologies for diagnosing heart disease. In CEUR Workshop Proceedings, Vol. 2762.
[20]
Nils Svangard, Peter Nordin, and Stefan Lloyd. 2002. Parallel Evolution of Trading Strategies Based on Binary Classification Using Sub-Machine-Code Genetic Programming. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02).
[21]
Darrell Whitley, Soraya Rana, and Robert B. Heckendorn. 1999. The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7, 1 (1999).
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Published: 08 July 2021
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