Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Using Factorial Experiments to Evaluate the Effect of Genetic Programming Parameters
Proceedings of the European Conference on Genetic Programming
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Introducing time in reaction systems
Theoretical Computer Science
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using genetic algorithms to evolve behavior in cellular automata
UC'05 Proceedings of the 4th international conference on Unconventional Computation
Basic notions of reaction systems
DLT'04 Proceedings of the 8th international conference on Developments in Language Theory
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Reaction systems is a formalism inspired by chemical reactions introduced by Rozenberg and Ehrenfeucht. Recently, an evolutionary algorithm based on this formalism, called Evolutionary Reaction Systems, has been presented. This new algorithm proved to have comparable performances to other well-established machine learning methods, like genetic programming, neural networks and support vector machines on both artificial and real-life problems. Even if the results are encouraging, to make Evolutionary Reaction Systems an established evolutionary algorithm, an in depth analysis of the effect of its parameters on the search process is needed, with particular focus on those parameters that are typical of Evolutionary Reaction Systems and do not have a counterpart in traditional evolutionary algorithms. Here we address this problem for the first time. The results we present show that one particular parameter, between the ones tested, has a great influence on the performances of Evolutionary Reaction Systems, and thus its setting deserves practitioners' particular attention: the number of symbols used to represent the reactions that compose the system. Furthermore, this work represents a first step towards the definition of a set of default parameter values for Evolutionary Reaction Systems, that should facilitate their use for beginners or inexpert practitioners.