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.