Functional Dependency and Degeneracy: Detailed Analysis of the GAuGE System

This paper explores the mapping process of the GAuGE system, a recently introduced position-independent genetic algorithm, that encodes both the positions and the values of individuals at the genotypic level. A mathematical formalisation of its mapping process is presented, and is used to characterise the functional dependency feature of the system. An analysis of the effect of degeneracy in this functional dependency is then performed, and a mathematical theorem is given, showing that the introduction of degeneracy reduces the position specification bias of individuals. Experimental results are given, that backup these findings.

[1]  Wolfgang Banzhaf,et al.  Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming , 1994, PPSN.

[2]  Conor Ryan,et al.  A Re-examination Of The Cart Centering Problem Using The Chorus System , 2002, GECCO.

[3]  K. Holsinger The neutral theory of molecular evolution , 2004 .

[4]  T. Jukes,et al.  The neutral theory of molecular evolution. , 2000, Genetics.

[5]  Michael O'Neill,et al.  Genetic Algorithms Using Grammatical Evolution , 2002, EuroGP.

[6]  Conor Ryan,et al.  How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System , 2003, EuroGP.

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

[8]  Maarten Keijzer,et al.  Crossover in Grammatical Evolution , 2003, Genetic Programming and Evolvable Machines.

[9]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[10]  Michael O'Neill,et al.  Grammatical Evolution: Evolving Programs for an Arbitrary Language , 1998, EuroGP.

[11]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[12]  John Daniel. Bagley,et al.  The behavior of adaptive systems which employ genetic and correlation algorithms : technical report , 1967 .

[13]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[14]  G. Harik Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms , 1997 .