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View all- Golemanova EGolemanov T(2022)Genetic Algorithms in a Visual Declarative ProgrammingWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2022.19.1419(138-152)Online publication date: 21-Jun-2022
Implementation matters, also in concurrent evolutionary algorithms
Pages 1591 - 1598
Abstract
Concurrency in evolutionary algorithms allow researchers to leverage the performance of powerful multi-core desktop architectures by parallelizing tasks using a high-level interface. However, concurrency also introduces additional complexity at the model and the implementation level. In this paper we describe how using parallel execution monitoring tools to check the effective parallelism of the implementation, can help to work out some of said complexities at the implementation-level, which in turn, are translated into improvements at the algorithmic-level. The performance gain is noticeable from an evaluations/seconds point of view to the possible scaling that can be achieved in the running machine, up to a superlinear scaling in an off-the-shelf platfonn. We show that the design using communicating sequential processes implemented in the language Raku is the basis for these improvements.
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Published: 08 July 2020
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View all- Golemanova EGolemanov T(2022)Genetic Algorithms in a Visual Declarative ProgrammingWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2022.19.1419(138-152)Online publication date: 21-Jun-2022
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