A new model of simulated evolutionary computation - Convergence analysis and specifications

There have been various algorithms designed for simulating natural evolutio n. This paper proposes a new simulated evolutionary computation model calle d the abstract evolutionary algorithm (AEA), which unifies most of the curr ently known evolutionary algorithms and describes the evolution as an abstr act stochastic process composed of two fundamental operators: selection and evolution operators, By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorit hms, directly yielding new convergence conditions and convergence rate esti mations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation.