Solving identification problem for asynchronous finite state machines using genetic algorithms

Authors:
Xiaojun Geng
Affiliations:
California State University, Northridge, CA
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 2
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Switching and Finite Automata Theory: Computer Science Series

Switching and Finite Automata Theory: Computer Science Series
Genetic Simulation for Finite State Machine Identification

SS '99 Proceedings of the Thirty-Second Annual Simulation Symposium

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Abstract

A Genetic Algorithm, embedded in a simulation-based method, is applied to the identification of Asynchronous Finite State Machines. Two different coding schemes and their associated crossover operations are examined. It is shown that one operator / coding pair outperforms the other in that the scheme reduces noticeably the production of invalid chromosomes thus increasing the efficiency and the convergence rate of the evolution process.