Exploring Grammatical Modification with Modules in Grammatical Evolution

There have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification with a grammar-based approach to genetic programming, grammatical evolution (GE). Here, GE's grammar was modified over the course of an evolutionary run with modules in order to facilitate their appearance in the population. This is the first step in what will be a series of analysis on methods of modifying GE's grammar to enhance evolutionary performance. The results show that identifying modules and using them to modify GE's grammar can have a negative effect on search performance when done improperly. But, if undertaken thoughtfully, there are possible benefits to dynamically enhancing the grammar with modules identified during evolution.

[1]  Julian Francis Miller,et al.  The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming , 2008, IEEE Transactions on Evolutionary Computation.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[4]  Peter J. Angeline,et al.  Evolutionary Module Acquisition , 1993 .

[5]  John R. Koza,et al.  Architecture-Altering Operations for Evolving the Architecture of a Multi-Part Program in Genetic Programming , 1994 .

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

[7]  Peter A. Whigham Inductive bias and genetic programming , 1995 .

[8]  Leonardo Vanneschi,et al.  Open issues in genetic programming , 2010, Genetic Programming and Evolvable Machines.

[9]  Sea Ling,et al.  Describing Web Service Architectures through Design-by-Contract , 2003, ISCIS.

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

[11]  Alan Blair,et al.  Dynamically Defined Functions In Grammatical Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  N. Chater,et al.  Proceedings of the fourteenth annual conference of the cognitive science society , 1992 .

[13]  Riccardo Poli,et al.  Genetic and Evolutionary Computation – GECCO 2004 , 2004, Lecture Notes in Computer Science.

[14]  Conor Ryan,et al.  Favourable Biasing of Function Sets Using Run Transferable Libraries , 2005 .

[15]  Erik Anders,et al.  An Exploration of Grammars in Grammatical Evolution , 2010 .

[16]  Erik Hemberg An exploration of learning and grammars in grammatical evolution , 2009, GECCO '09.

[17]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

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

[19]  J. Pollack,et al.  The Evolutionary Induction of Subroutines , 1997 .

[20]  Anthony Brabazon,et al.  An investigation into automatically defined function representations in Grammatical Evolution , 2009 .

[21]  Conor Ryan,et al.  Run Transferable Libraries - Learning Functional Bias in Problem Domains , 2004, GECCO.

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

[23]  Conor Ryan,et al.  Context-aware mutation: a modular, context aware mutation operator for genetic programming , 2007, GECCO '07.