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. © 2012 Springer-Verlag.
Manzoni, L., Castelli, M., Vanneschi, L. (2012). Evolutionary reaction systems. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Proceedings of the 10th European Conference, EvoBIO 2012 (pp.13-25) [10.1007/978-3-642-29066-4_2].
Evolutionary reaction systems
MANZONI, LUCA;CASTELLI, MAURO;VANNESCHI, LEONARDO
2012
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. © 2012 Springer-Verlag.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
