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12-05-2020 | Methodologies and Application

A novel technique to self-adapt parameters in parallel/distributed genetic programming

Author: Marco Russo

Published in: Soft Computing | Issue 22/2020

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Abstract

This paper introduces the Supervisor Evolutionary Algorithm, a novel technique that allows for self-adapt almost all the internal parameters in parallel distributed client-server genetic programming. This novel adapting mechanism, is itself of an evolutionary nature, so we have a double evolutionary tool. The upper level, as is usual in evolutionary computing, has its own customized selection, crossover, and mutation mechanisms. The lower stage used here is the Brain Project a parallel-distributed software tool for formal modelling of numerical data using a hybrid neural-genetic programming technique. As demonstrated by the experiment reported in this paper, our approach works well adapting continuously its internal parameters.

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Title: A novel technique to self-adapt parameters in parallel/distributed genetic programming
Author: Marco Russo
Publication date: 12-05-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 22/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04982-w

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