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.
Si
paper
Computer Science (all); Theoretical Computer Science
English
10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
9783642290657
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].
Manzoni, L; Castelli, M; Vanneschi, L
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/60803
Citazioni
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
Social impact