Equilibrium States of Iterated Random Maps Arising in Evolutionary Algorithms


The equlibrium states and the dynamical entropy of iterated random maps that arise in modeling a class of evolutionary algorithms were studied. An equilibrium state is characterized as a measure that maximizes a linear combination of an entropy and energy-like quantity. The iterated random maps are governed by a collection of mappings that are selected randomly with some probability distribution. Results show that characterization of the equilibrium state and the dynamical entropy in this model opens the possibility of studying the structural stability of evolutionary algorithms.

Publication Title

Proceedings of the Joint Conference on Information Sciences

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