TY - GEN
T1 - Evolutionary reaction systems
AU - Manzoni, Luca
AU - Castelli, Mauro
AU - Vanneschi, Leonardo
PY - 2012/4/3
Y1 - 2012/4/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84859133332&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-29066-4_2
DO - 10.1007/978-3-642-29066-4_2
M3 - Conference contribution
AN - SCOPUS:84859133332
SN - 9783642290657
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 25
BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 10th European Conference, EvoBIO 2012, Proceedings
T2 - 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012
Y2 - 11 April 2012 through 13 April 2012
ER -