Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The quark and the jaguar: adventures in the simple and the complex
The quark and the jaguar: adventures in the simple and the complex
An introduction to genetic algorithms
An introduction to genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Sequential Machines: Selected Papers
Sequential Machines: Selected Papers
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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A reaction network arises when a set of reactants (chromosomes, chemicals, economic goods, or the like) recombine at specified rates to produce other reactants in the set. When the reactants are characterized in terms of "reactive regions" (schemata, active sites, building blocks), reaction networks can be modeled by classic stochastic urn models. The corresponding Markov processes are specified by matrices that, for realistic problems, are small enough to allow standard matrix operations and Monte Carlo estimates of important properties of the trajectory of the process, such as the expected time to first occurrence of some designated reactant.