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.

Keywords

Genetic Programming Input Symbol Computational Formalism Cartesian Genetic Program Output Symbol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luca Manzoni
    • 1
  • Mauro Castelli
    • 1
  • Leonardo Vanneschi
    • 2
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo)Univesità degli Studi di Milano-BicoccaMilanoItaly
  2. 2.ISEGIUniversidade Nova de LisboaLisboaPortugal

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