Neural networks primer, part I
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Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
PADO: a new learning architecture for object recognition
Symbolic visual learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proceedings of the European Conference on Genetic Programming
Linear-Tree GP and Its Comparison with Other GP Structures
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Linear-Graph GP - A New GP Structure
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Strongly typed genetic programming
Evolutionary Computation
Introducing time in reaction systems
Theoretical Computer Science
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IBM Journal of Research and Development
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Basic notions of reaction systems
DLT'04 Proceedings of the 8th international conference on Developments in Language Theory
IEEE Transactions on Evolutionary Computation
Parameter tuning of evolutionary reactions systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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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.