An investigation on the roles of insertion and deletion operators in tree adjoining grammar guided genetic programming

We investigate the roles of insertion and deletion as mutation operators and local search operators in a tree adjoining grammar guided genetic programming (TAG3P) system (Nguyen Xuan Hoai et al., 2003). The results show that, on three standard problems, these operators work better as mutation operators than the more standard sub-tree mutation originally used in (Nguyen Xuan Hoai et al., 2003, 2004). Moreover, for some problems, insertion and deletion can act effectively as local search operators, allowing TAG3P to solve problems with very small population sizes.

[1]  Aravind K. Joshi,et al.  Mathematical and computational aspects of lexicalized grammars , 1990 .

[2]  Hussein A. Abbass,et al.  Toward an Alternative Comparison between Different Genetic Programming Systems , 2004, EuroGP.

[3]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[4]  Alexander A. Stepanov,et al.  Generic Programming , 1988, ISSAC.

[5]  A. H. Aguirre,et al.  Gate-level synthesis of Boolean functions using binary multiplexers and genetic programming , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[6]  Nader Vadiee Fuzzy rule-based expert systems II , 1993 .

[7]  Aravind K. Joshi,et al.  A study of tree adjoining grammars , 1987 .

[8]  Carlos A. Coello Coello,et al.  A genetic programming approach to logic function synthesis by means of multiplexers , 1999, Proceedings of the First NASA/DoD Workshop on Evolvable Hardware.

[9]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming , 1999, Genetic Programming and Evolvable Machines.

[10]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[11]  Aravind K. Joshi,et al.  Tree Adjunct Grammars , 1975, J. Comput. Syst. Sci..

[12]  Stuart M. Shieber,et al.  An Alternative Conception of Tree-Adjoining Derivation , 1992, ACL.

[13]  Andreas Geyer-Schulz,et al.  Fuzzy Rule-Based Expert Systems and Genetic Machine Learning , 1996 .

[14]  Peter A. Whigham,et al.  Grammatical bias for evolutionary learning , 1996 .

[15]  Aravind K. Joshi,et al.  Tree-Adjoining Grammars , 1997, Handbook of Formal Languages.

[16]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[17]  Nguyen Xuan Hoai,et al.  A Framework For Tree-Adjunct Grammar Guided Genetic Programming , 2001 .

[18]  David J. Weir,et al.  Characterizing mildly context-sensitive grammar formalisms , 1988 .

[19]  Jason M. Daida,et al.  What Makes a Problem GP-Hard? Validating a Hypothesis of Structural Causes , 2003, GECCO.

[20]  Man Leung Wong,et al.  Evolutionary Program Induction Directed by Logic Grammars , 1997, Evolutionary Computation.

[21]  Nguyen Xuan Hoai,et al.  Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators , 2004, GECCO.

[22]  Nguyen Xuan Hoai,et al.  Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Richard C. Waters,et al.  Tree Insertion Grammar: A Cubic-Time, Parsable Formalism that Lexicalizes Context-Free Grammar without Changing the Trees Produced , 1995, CL.

[24]  Peter A. Whigham,et al.  Grammatically-based Genetic Programming , 1995 .

[25]  Hussein A. Abbass,et al.  Tree Adjoining Grammars, Language Bias, and Genetic Programming , 2003, EuroGP.