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.

Island model in ActoDatA: An actor-based implementation of a classical distributed evolutionary computation paradigm / Petrosino, G.; Bergenti, F.; Lombardo, G.; Mordonini, M.; Poggi, A.; Tomaiuolo, M.; Cagnoni, S.. - (2021), pp. 1801-1808. ((Intervento presentato al convegno 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 nel 2021 [10.1145/3449726.3463210].

Island model in ActoDatA: An actor-based implementation of a classical distributed evolutionary computation paradigm

Petrosino G.;Bergenti F.;Lombardo G.;Mordonini M.;Poggi A.;Tomaiuolo M.;Cagnoni S.
2021

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.
9781450383516
Island model in ActoDatA: An actor-based implementation of a classical distributed evolutionary computation paradigm / Petrosino, G.; Bergenti, F.; Lombardo, G.; Mordonini, M.; Poggi, A.; Tomaiuolo, M.; Cagnoni, S.. - (2021), pp. 1801-1808. ((Intervento presentato al convegno 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 nel 2021 [10.1145/3449726.3463210].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2905168
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