J. Manuel Colmenar
ABSTRACT
We propose an evolutionary flow for finite state machine inference through the cooperation of grammatical evolution and a genetic algorithm. This coevolution has two main advantages. First, a high-level description of the target problem is accepted by the flow, being easier and affordable for system designers. Second, the designer does not need to define a training set of input values because it is automatically generated by the genetic algorithm at run time. Our experiments on the sequence recognizer and the vending machine problems obtained the FSM solution in 99.96% and 100% of the optimization runs, respectively.
- JECO: Java Evolutionary Computation library. https://code.google.com/p/paba/.Google Scholar
- C. Johnson. Genetic programming with fitness based on model checking. In M. Ebner, M. O'Neill, A. Ekárt, L. Vanneschi, and A. Esparcia-Alcázar, editors, Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 114--124. Springer Berlin / Heidelberg, 2007. Google ScholarDigital Library
- M. O'Neill, E. Hemberg, C. Gilligan, E. Bartley, J. McDermott, and A. Brabazon. GEVA - grammatical evolution in Java. SIGEVOlution, 3(2):17--22, 2008. Google ScholarDigital Library
- M. O. C. Ryan. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell, MA, USA, 2003. Google ScholarDigital Library
- F. Tsarev and K. Egorov. Finite state machine induction using genetic algorithm based on testing and model checking. In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, GECCO '11, pages 759--762, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
Index Terms
- An evolutionary methodology for automatic design of finite state machines
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