A Genetic Algorithm for the Induction of Nondeterministic Pushdown Automata

This paper presents a genetic algorithm used to infer pushdown automata from legal and illegal examples of a language. It gives an introduction into grammatical inference, and discusses related work in grammatical inference using genetic algorithms. The paper describes the type of automaton that is used, the evaluation of the fitness of automata with respect to a set of examples of a language, the representation of automata in the genetic algorithm, and the genetic operators that work on this representation. Results are reported on the inference of a test suite of 10 languages. Pushdown automata for the language of correctly balanced and nested parentheses expressions, the language of sentences containing an equal number of a’s and b’s, the two-symbol palindromes, a set of regular languages, and a small natural language subset were inferred. Furthermore, some possible improvements and extensions of the algorithm are discussed.

[1]  Peter Ross,et al.  Dynamic Training Subset Selection for Supervised Learning in Genetic Programming , 1994, PPSN.

[2]  P. Wyard.  Representational Issues for Context Free Grammar Induction Using Genetic Algorithms , 1994, ICGI.

[3]  Pierre Dupont,et al.  Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method , 1994, ICGI.

[4]  Simon M. Lucas,et al.  Structuring chromosomes for context-free grammar evolution , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[5]  Marc M. Lankhorst.  Grammatical Inference with a Genetic Algorithm , 1994, EUROSIM.

[6]  Rafael C. Carrasco,et al.  Grammatical Inference and Applications , 1994, Lecture Notes in Computer Science.

[7]  Willem-Olaf Huijsen,et al.  Genetic Grammatical Inference , 1994 .

[8]  Peter J. Angeline.  Evolutionary algorithms and emergent intelligence , 1993 .

[9]  Sandip Sen,et al.  Learning to construct pushdown automata for accepting deterministic context-free languages , 1992, Defense, Security, and Sensing.

[10]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[11]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[12]  Peter J. Wyard.  Context Free Grammar Induction Using Genetic Algorithms , 1991, ICGA.

[13]  Raymond L. Watrous,et al.  Induction of Finite-State Automata Using Second-Order Recurrent Networks , 1991, NIPS.

[14]  C. Lee Giles,et al.  Extracting and Learning an Unknown Grammar with Recurrent Neural Networks , 1991, NIPS.

[15]  Peter D. Turney.  The gap between abstract and concrete results in machine learning , 1991, J. Exp. Theor. Artif. Intell..

[16]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[17]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[18]  C. Lee Giles,et al.  Higher Order Recurrent Networks and Grammatical Inference , 1989, NIPS.

[19]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[20]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[21]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, CACM.

[22]  Carl H. Smith,et al.  Inductive Inference: Theory and Methods , 1983, CSUR.

[23]  Albert Donally Bethke,et al.  Genetic Algorithms as Function Optimizers , 1980 .

[24]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[25]  DANA ANGLUIN,et al.  On the Complexity of Minimum Inference of Regular Sets , 1978, Inf. Control..

[26]  Taylor L. Booth,et al.  Grammatical Inference: Introduction and Survey-Part I , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[28]  Jay Earley,et al.  An efficient context-free parsing algorithm , 1970, Commun. ACM.

[29]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[30]  A. Bennett.  The Origin of Species by means of Natural Selection; or the Preservation of Favoured Races in the Struggle for Life , 1872, Nature.

[31]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .