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Logicbased Genetic Programming with Definite Clause Translation Grammars
 NEW GENERATION COMPUTING
, 2001
"... DCTGGP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context–free languages, and it allows semantic information associated with a language to be easily accomodated by the grammar. T ..."
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Cited by 20 (10 self)
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DCTGGP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context–free languages, and it allows semantic information associated with a language to be easily accomodated by the grammar. This is useful in genetic programming for defining the interpreter of a target language, or incorporating both syntactic and semantic problem–specific contraints into the evolutionary search. The DCTGGP system improves on other grammar–based GP systems by permitting non–trivial semantic aspects of the language to be defined with the grammar. It also automatically analyzes grammar rules in order to determine their minimal depth and termination characteristics, which are required when generating random program trees of varied shapes and sizes. An application using DCTGGP is described.
Evolving Stochastic ContextFree Grammars from Examples Using a Minimum Description Length Principle
 Paper presented at the Workshop on Automata Induction Grammatical Inference and Language Acquisition, ICML97
"... This paper describes an evolutionary approach to the problem of inferring stochastic contextfree grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem are evolv ..."
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This paper describes an evolutionary approach to the problem of inferring stochastic contextfree grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. We provide details of our fitness function for grammars and present the results of a number of experiments in learning grammars for a range of formal languages. Keywords: grammatical inference, genetic algorithms, language modelling, formal languages, induction, minimum description length. Introduction Grammatical inference (Gold 1978) is a fundamental problem in many areas of artificial intelligence and cognitive science, including speech and language processing, syntactic pattern recognition and automated programming. Although a wide variety of techniques for automated grammatical inference have been devi...
Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions
 International Journal of Applied Intelligence
, 1999
"... The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for effi ..."
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Cited by 9 (5 self)
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The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognize a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cutoff value will preempt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognizing stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.
An Indexed Bibliography of Distributed Genetic Algorithms
, 1999
"... s: Jan. 1995 { Sep. 1998 ACM: ACM Guide to Computing Literature: 1979  1993/4 BA: Biological Abstracts: July 1996  Aug. 1998 CA: Computer Abstracts: Jan. 1993 { Feb. 1995 CCA: Computer & Control Abstracts: Jan. 1992 { Apr. 1998 (except May95) ChA: Chemical Abstracts: Jan. 1997  Dec. 19 ..."
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Cited by 7 (1 self)
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s: Jan. 1995 { Sep. 1998 ACM: ACM Guide to Computing Literature: 1979  1993/4 BA: Biological Abstracts: July 1996  Aug. 1998 CA: Computer Abstracts: Jan. 1993 { Feb. 1995 CCA: Computer & Control Abstracts: Jan. 1992 { Apr. 1998 (except May95) ChA: Chemical Abstracts: Jan. 1997  Dec. 1998 CTI: Current Technology Index Jan./Feb. 1993 { Jan./Feb. 1994 DAI: Dissertation Abstracts International: Vol. 53 No. 1 { Vol. 56 No. 10 (Apr. 1996) EEA: Electrical & Electronics Abstracts: Jan. 1991 { Apr. 1998 EI A: The Engineering Index Annual: 1987  1992 EI M: The Engineering Index Monthly: Jan. 1993 { Apr. 1998 (except May 1997) N: Scientic and Technical Aerospace Reports: Jan. 1993  Dec. 1995 (except Oct. 1995) P: Index to Scientic & Technical Proceedings: Jan. 1986 { May 1998 (except Nov. 1994) PA: Physics Abstracts: Jan. 1997 { Sep. 1998 1.1 Your contributions erroneous or missing? The bibliography database is updated on a regular basis and certain...
Learning Stochastic ContextFree Grammars from Corpora Using a Genetic Algorithm
 University of Sussex
, 1997
"... A genetic algorithm for inferring stochastic contextfree grammars from finite language samples is described. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. We describe a number of experiments in learning grammars for a ..."
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Cited by 6 (1 self)
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A genetic algorithm for inferring stochastic contextfree grammars from finite language samples is described. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. We describe a number of experiments in learning grammars for a range of formal languages. The results of these experiments are encouraging and compare very favourably with other approaches to stochastic grammatical inference. Keywords: grammatical inference, genetic algorithms, stochastic grammar, formal languages, induction. 1 Introduction This paper describes an evolutionary approach to the problem of inferring stochastic contextfree grammars from finite language samples or corpora. Grammatical inference [Go78] is a fundamental problem in many areas of artificial intelligence and cognitive science, including speech and language processing, syntactic pattern recognition and automated programming. Although a wide variety of techniques for automated g...
An Approach to the Automatic Acquisition of Phonotactic Constraints
 In T
, 1998
"... This paper describes a formal approach and a practical learning method for automatically acquiring phonotactic constraints encoded as finite automata. It is proposed that the use of different classes of syllables with classspecific intrasyllabic phonotactics results in a more accurate hypothesis o ..."
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This paper describes a formal approach and a practical learning method for automatically acquiring phonotactic constraints encoded as finite automata. It is proposed that the use of different classes of syllables with classspecific intrasyllabic phonotactics results in a more accurate hypothesis of a language's phonological grammar than the single syllable class traditionally used. Intrasyllabic constraints are encoded as acyclic finite automata with input alphabets of phonetalc symbols. These automata in turn form the transitions in cyclic finite automata that encode the intersyllabic constraints of wordlevel phonology. A genetic algorithm is used to automatically construct finite automata from training sets of symbol strings. Results are reported for a set of German syllables and a set of Russian bisyllabic feminine nouns.
A Genetic Algorithm for Finite State Automata Induction with an Application to Phonotactics
"... This paper presents a genetic algorithm for the automatic construction of nitestate automata from positive data. The algorithm is suitable for constructing automata from complete and incomplete presentation of data. In the case of incomplete presentation of data, different degrees of generalisation ..."
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This paper presents a genetic algorithm for the automatic construction of nitestate automata from positive data. The algorithm is suitable for constructing automata from complete and incomplete presentation of data. In the case of incomplete presentation of data, different degrees of generalisation can be achieved with a set of search parameters. This paper describes the algorithm, and presents results for data sets of German syllables and Russian bisyllabic words.
An Empirical Investigation of an Evolutionary Algorithm's Ability to Maintain a Known Good Solution
 In Evolutionary Programming VII
, 1998
"... . We analyze the disruptiveness of four operators in an evolutionary algorithm("EA") solving a grammar induction problem. The EA in question contains mutation, crossover, inversion, and substitution operators. Grammars are encoded on genotypes in a representation which includes variablelength i ..."
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Cited by 1 (1 self)
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. We analyze the disruptiveness of four operators in an evolutionary algorithm("EA") solving a grammar induction problem. The EA in question contains mutation, crossover, inversion, and substitution operators. Grammars are encoded on genotypes in a representation which includes variablelength introns. A repeated measures analysis of variance ("ANOVA") with four factors, the four operators' rates, is used. It is discovered that some operator rates interact, meaning that their effects on the EA's performance when used together are more than the sum of their individual effects. In particular, this is found to be true for crossover and mutation, even for a low mutation rate. This suggests that analyzing these operators separately is inadequate, and that operators must be studied in combination, instead of in isolation. 1 Overview The degree to which operators are disruptive is a longstanding question in evolutionary computation. Most often, however, each operator is investig...
Inference of Russian Phonotactics with a Genetic Algorithm
, 1998
"... This paper describes results for a set of experiments in which a genetic algorithm was used to infer Russian phonotactic constraints encoded as a finitestate automaton. It was found that small changes to the data set used as a basis for the inference task can have a strong effect on the learnabilit ..."
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This paper describes results for a set of experiments in which a genetic algorithm was used to infer Russian phonotactic constraints encoded as a finitestate automaton. It was found that small changes to the data set used as a basis for the inference task can have a strong effect on the learnability of individual strings and of the task as a whole.