Manzoni, L., Castelli, M., & Vanneschi, L. (2012, April). Evolutionary reaction systems. In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (pp. 13-25). Springer, Berlin, Heidelberg.
Abstract
In the recent years many bio-inspired computational methods were defined and successfully applied to real life problems. Examples of those methods are particle swarm optimization, ant colony, evolutionary algorithms, and many others. At the same time, computational formalisms inspired by natural systems were defined and their suitability to represent different functions efficiently was studied. One of those is a formalism known as reaction systems. The aim of this work is to establish, for the first time, a relationship between evolutionary algorithms and reaction systems, by proposing an evolutionary version of reaction systems. In this paper we show that the resulting new genetic programming system has better, or at least comparable performances to a set of well known machine learning methods on a set of problems, also including real-life applications. Furthermore, we discuss the expressiveness of the solutions evolved by the presented evolutionary reaction systems.
Bibtex file
@inproceedings{manzoni2012evolutionary,
title={Evolutionary reaction systems},
author={Manzoni, Luca and Castelli, Mauro and Vanneschi, Leonardo},
booktitle={European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics},
pages={13--25},
year={2012},
organization={Springer}
}
Manzoni, L., Castelli, M., & Vanneschi, L. (2012, April). Evolutionary reaction systems. In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (pp. 13-25). Springer, Berlin, Heidelberg.
Abstract In the recent years many bio-inspired computational methods were defined and successfully applied to real life problems. Examples of those methods are particle swarm optimization, ant colony, evolutionary algorithms, and many others. At the same time, computational formalisms inspired by natural systems were defined and their suitability to represent different functions efficiently was studied. One of those is a formalism known as reaction systems. The aim of this work is to establish, for the first time, a relationship between evolutionary algorithms and reaction systems, by proposing an evolutionary version of reaction systems. In this paper we show that the resulting new genetic programming system has better, or at least comparable performances to a set of well known machine learning methods on a set of problems, also including real-life applications. Furthermore, we discuss the expressiveness of the solutions evolved by the presented evolutionary reaction systems.
Link to the online copy
Bibtex file @inproceedings{manzoni2012evolutionary, title={Evolutionary reaction systems}, author={Manzoni, Luca and Castelli, Mauro and Vanneschi, Leonardo}, booktitle={European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}, pages={13--25}, year={2012}, organization={Springer} }