Grammatical bias and building blocks in meta-grammar Grammatical Evolution

This paper describes and tests the utility of a meta grammar approach to grammatical evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE, under a meta grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved and directly incorporated into the grammar during a run. This approach facilitates the evolution of modularity and reuse both on structural and symbol levels and consequently could enhance both the scalability of GE and its adaptive potential in dynamic environments. In this paper an analysis of the extent that building block structures created in the grammars are used in the solution is undertaken. It is demonstrated that building block structures are incorporated into the evolving grammars and solutions at a rate higher than would be expected by random search. Furthermore, the results indicate that grammar design can be an important factor in performance.

[1]  Wolfgang Banzhaf,et al.  The evolution of genetic code in Genetic Programming , 1999 .

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  David M. W. Powers,et al.  Evolvability and Redundancy in Shared Grammar Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[4]  A. Sima Etaner-Uyar,et al.  Towards an analysis of dynamic environments , 2005, GECCO '05.

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  Malcolm I. Heywood,et al.  Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings , 2007, Genetic Programming and Evolvable Machines.

[7]  Wojciech Piaseczny,et al.  Chemical Genetic Programming – evolutionary optimization of the genotype-to-phenotype translation set , 2005, Artificial Life and Robotics.

[8]  Conor Ryan,et al.  Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code , 2004, EuroGP.

[9]  Anthony Brabazon,et al.  Altering Search Rates of the Meta and Solution Grammars in the mGGA , 2008, EuroGP.

[10]  Michael O'Neill,et al.  Grammatical evolution - evolutionary automatic programming in an arbitrary language , 2003, Genetic programming.

[11]  Hussein A. Abbass,et al.  Grammar model-based program evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Antonia J. Jones,et al.  An Adaptive Mapping for Developmental Genetic Programming , 2001, EuroGP.

[13]  Anthony Brabazon,et al.  mGGA: The meta-Grammar Genetic Algorithm , 2005, EuroGP.

[14]  D. C. Cooper,et al.  Sequential Machines and Automata Theory , 1968, Comput. J..

[15]  Anthony Brabazon,et al.  A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms , 2007, EuroGP.

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

[17]  Anthony Brabazon,et al.  Meta-grammar constant creation with grammatical evolution by grammatical evolution , 2005, GECCO '05.

[18]  Annie S. Wu,et al.  The Modular Genetic Algorithm: Exploiting Regularities in the Problem Space , 2003, ISCIS.

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .