Fine grained population diversity analysis for parallel genetic programming

In this paper we describe a formalism for estimating the structural similarity of formulas that are evolved by parallel genetic programming (GP) based identification processes. This similarity measurement can be used for measuring the genetic diversity among GP populations and, in the case of multi-population GP, the genetic diversity among sets of GP populations: The higher the average similarity among solutions becomes, the lower is the genetic diversity. Using this definition of genetic diversity for GP we test several different GP based system identification algorithms for analyzing real world measurements of a BMW Diesel engine as well as medical benchmark data taken from the UCI machine learning repository.

[1]  Anikó Ekárt,et al.  A Metric for Genetic Programs and Fitness Sharing , 2000, EuroGP.

[2]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[3]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

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

[5]  Michael Affenzeller,et al.  HeuristicLab: A Generic and Extensible Optimization Environment , 2005 .

[6]  Stephan M. Winkler,et al.  Evolutionary System Identification , 2009 .

[7]  Michael Affenzeller,et al.  SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results , 2004, J. Heuristics.

[8]  N. Hopper,et al.  Analysis of genetic diversity through population history , 1999 .

[9]  Graham Kendall,et al.  Diversity in genetic programming: an analysis of measures and correlation with fitness , 2004, IEEE Transactions on Evolutionary Computation.

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

[11]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[12]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[13]  Maarten Keijzer,et al.  Efficiently representing populations in genetic programming , 1996 .

[14]  Stephan M. Winkler,et al.  Benefits of Plugin-Based Heuristic Optimization Software Systems , 2007, EUROCAST.

[15]  Stephan M. Winkler,et al.  Goal-oriented preservation of essential genetic information by offspring selection , 2005, GECCO '05.

[16]  Stephan M. Winkler,et al.  Fine-grained population diversity estimation for genetic programming based structure identification , 2008, GECCO '08.

[17]  Robert I. McKay,et al.  Fitness Sharing in Genetic Programming , 2000, GECCO.

[18]  Stephan M. Winkler,et al.  On the Reliability of Nonlinear Modeling using Enhanced Genetic Programming Techniques , 2009 .