Program distribution estimation with grammar models

This research extends conventional Estimation of Distribution Algorithms (EDA) to Genetic Programming (GP) domain. We propose a framework to estimate the distribution of solutions in tree form. The core of this framework is a grammar model. In this research, we show, both theoretically and experimentally, that a grammar model has many of the properties we need for estimation of distribution for tree form solutions. We report one of our implementations of this framework. Experimental study confirms the relevance of this framework to problem solving.

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

[2]  Patrik D'haeseleer,et al.  Context preserving crossover in genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[3]  David E. Goldberg,et al.  Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .

[4]  Dana H. Ballard,et al.  Genetic Programming with Adaptive Representations , 1994 .

[5]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

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

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

[8]  J. Pollack,et al.  Coevolving High-Level Representations , 1993 .

[9]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[10]  Mark Craven,et al.  Refining the Structure of a Stochastic Context-Free Grammar , 2001, IJCAI.

[11]  Hussein A. Abbass,et al.  AntTAG: a new method to compose computer programs using colonies of ants , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

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

[14]  Corso Elvezia.  Probabilistic Incremental Program Evolution , 1997 .

[15]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[16]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[17]  P. Bosman,et al.  An algorithmic framework for density estimation based evolutionary algorithms , 1999 .

[18]  G. Harik.  Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[19]  Ivan Tanev,et al.  Implications of Incorporating Learning Probabilistic Context-sensitive Grammar in Genetic Programming on Evolvability of Adaptive Locomotion Gaits of Snakebot , 2004 .

[20]  Daryl Essam,et al.  Software project effort estimation using genetic programming , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.

[21]  H. Iba,et al.  Estimation of distribution programming based on Bayesian network , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[22]  D. Goldberg,et al.  Probabilistic Model Building and Competent Genetic Programming , 2003 .

[23]  David J. Montana,et al.  Strongly Typed Genetic Programming , 1995, Evolutionary Computation.

[24]  D. Rumelhart,et al.  Predicting sunspots and exchange rates with connectionist networks , 1991 .

[25]  Hussein A. Abbass,et al.  Program Evolution with Explicit Learning: a New Framework for Program Automatic Synthesis , 2003 .

[26]  Peter A. N. Bosman,et al.  Grammar Transformations in an EDA for Genetic Programming , 2004 .