Adaptive Finite State Automata and Genetic Algorithms: Merging Individual Adaptation and Population Evolution

摘要

This paper presents adaptive finite state automata as an alternative formalism to model individuals in a genetic algorithm environment. Adaptive finite automata, which are basically finite state automata that can change their internal structures during operation, have proven to be an attractive way to represent simple learning strategies. We argue that the merging of adaptive finite state automata and GA results in an elegant and appropriate environment to explore the impact of individual adaptation, during lifetime, on population evolution.