An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines

Evolutionary programming was first offered as an alternative method for generating artificial intelligence. Experiments were offered in which finite state machines were used to predict time series with respect to an arbitrary payoff function. Mutations were imposed on the evolving machines such that each of the possible modes of variation were given equal probability. The current study investigates the use of self-adaptive methods of evolutionary programming on finite state machines. Each machine incorporates a coding for its structure and an additional set of parameters that determine in part how it will distribute new trials. Two methods for accomplishing this self-adaptation are implemented and tested on two simple prediction problems. The results appear to favor the use of such self-adaptive methods.

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