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Structuring Chromosomes for Context-Free Grammar Evolution (1994) [14 citations — 4 self]

by Simon Lucas
in Proceedings of IEEE International Conference on Evolutionary Computation
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Abstract:

This paper investigates the use of genetic algorithms for inferring small regular and context-free grammars. Applied simply, a genetic algorithm is not very effective at this. To overcome this problem we investigate two methods of structuring the chromosomes. The first is to bias the distribution of `1's in the population of chromosomes according to an algebraic expansion technique previously developed by the author. This `design' of the chromosome distribution, shows no bias to any particular type of language (i.e. full generality is retained) yet improves convergence. The second method involves performing the evolution (i.e. making the mutations) in a different space, where the grammars are represented in `embedded normal form'. The latter approach structures the chromosome to represent context-free rather than regular grammars, for example. It is shown that biasing the chromosome in this fashion produces extremely fast convergence, and 3-symbol palindromes grammars are learned in ty...

Citations

258 The estimation of stochastic context-free grammars using the Inside-Outside Algorithm – Lari, Young - 1990
250 Designing Neural Networks Using Genetic Algorithms with Graph Generation System – Kitano - 1990
106 Ecient Parsing for Natural Language: a Fast Algorithm for Practical Systems – Tomita - 1986
103 Recognition and parsing of context-free languages in time n – Younger - 1967
71 Biological metaphors and the design of modular artificial neural networks – Boers, Kuiper - 1992
49 Cellular encoding of genetic neural networks. Technical Repport, rap-port No 92-21, Ecole Normale Superieure de – Gruau - 1992
11 Context-free grammar induction using genetic algorithms – Wyard - 1991
1 An algebraic approach to learning in syntactic neural networks – Lucas - 1992
1 Algebraic grammatical inference – Lucas - 1993