IS AMBIGUITY USEFUL OR PROBLEMATIC FOR GRAMMAR GUIDED GENETIC PROGRAMMING? A CASE STUDY
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by N. X. Hoai, Y. Shan, Ri Mckay
http://www.cs.adfa.edu.au/~shanyin/publications/ambiguity.pdf
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Abstract:
In [2] Antonisse made a conjecture that unambiguous grammars are better candidates for grammar-guided genetic learning. In this paper, we empirically show that it is not always the case, especially when the structural ambiguity is boosted by semantic redundancies in the grammar. We also show that the search space (or genotype space) of grammar guided genetic programming (GGGP) is truly tree sets rather than string sets of formalisms. 1.
Citations
| 274 | Introduction to Formal Language theory – Harrison - 1978 |
| 249 | Tree adjunct grammars – Joshi, Levy, et al. - 1975 |
| 179 | Genetic Programming: An Introduction – Banzhaf, Nordin, et al. - 1998 |
| 175 | Genetic Programming: On the Programming of Computers by Natural Selection – Koza - 1992 |
| 101 | Characterizing mildly context-sensitive grammar formalisms – Weir - 1988 |
| 39 | Grammatically-based genetic programming – Whigham - 1995 |
| 9 | A framework for tree adjunct grammar guided genetic programming – Hoai, McKay - 2001 |
| 5 | Relationship between strong and weak generative power of formal systems – Joshi - 2000 |
| 3 | Solving Symbolic Regression with Tree Adjunct Grammar Guided Genetic Programming: The Comparative Results – Hoai, McKay, et al. - 2002 |
| 3 | Grammatical Bias for Evolutionary Learning, PhD. Thesis at – Whigham - 1996 |
| 1 | A Grammar-Based Genetic Algorithmâ, in Foundations of Genetic Algorithms, edited by G.J.E – Antonisse - 1991 |
| 1 | Grammars in Evolutionary Computation: A Review – Hoai, McKay, et al. |
