The minimum consistent DFA problem cannot be approximated within any polynomial
Journal of the ACM (JACM)
Digital Design: Principles and Practices
Digital Design: Principles and Practices
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Efficient Algorithms for the Inference of Minimum Size DFAs
Machine Learning
Fitness landscapes and evolvability
Evolutionary Computation
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Synthesis of Synchronous Sequential Logic Circuits from Partial Input/Output Sequences
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Improving Correctness of Finite-State Machine Synthesis from Multiple Partial Input/Output Sequences
EH '99 Proceedings of the 1st NASA/DOD workshop on Evolvable Hardware
Genetic Programming and Evolvable Machines
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Development Brings Scalability to Hardware Evolution
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Tutorial on agent-based modeling and simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
A bibliographical study of grammatical inference
Pattern Recognition
Avida-MDE: a digital evolution approach to generating models of adaptive software behavior
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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With resemblance of finite-state machines to some biological mechanisms in cells and numerous applications of finite automata in different fields, this paper uses analogies and metaphors to introduce an element of bio-plausibility to evolutionary grammatical inference. Inference of a finite-state machine that generalizes well over unseen input-output examples is an NP-complete problem. Heuristic algorithms exist to minimize the size of an FSM keeping it consistent with all the input-output sequences. However, their performance dramatically degrades in presence of noise in the training set. Evolutionary algorithms perform better for noisy data sets but they do not scale well and their performance drops as size or complexity of the target machine grows. Here, inspired by a biological perspective, an evolutionary algorithm with a novel representation and a new fitness function for inference of Moore finite-state machines of limited size is proposed and compared with one of the latest evolutionary techniques.