A Macroscopic Exact Schema Theorem and a Redeenition of Eeective Fitness for Gp with One-point Crossover

This paper extends recent results in the GP schema theory by formulating a proper exact schema theorem for GP with one-point crossover. This gives an exact expression for the expected number of instances of a schema at the next generation in terms of macroscopic quantities. This result allows the exact formulation of the notion of eeective tness in GP introduced, in approximate form, by other researchers to describe the reasons for bloat and active-code compression.

[1]  P.A. Whigham,et al.  A Schema Theorem for context-free grammars , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[2]  Riccardo Poli,et al.  A Review of Theoretical and Experimental Results on Schemata in Genetic Programming , 1998, EuroGP.

[3]  W. Langdon,et al.  Analysis of Schema Variance and Short Term Extinction Likelihoods , 2001 .

[4]  Riccardo Poli Schema Theory without Expectations for GP and GAs with One-Point Crossover in the Presence of Schema Creation , 1999 .

[5]  Christopher R. Stephens,et al.  Effective Degrees of Freedom in Genetic Algorithms and the Block Hypothesis , 1997, ICGA.

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

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

[8]  D. Wolpert,et al.  On 2-Armed Gaussian Bandits and Optimization , 1996 .

[9]  ProgrammingJustinian P. RoscaComputer Analysis of Complexity Drift in Genetic , 1997 .

[10]  Peter Nordin,et al.  Complexity Compression and Evolution , 1995, ICGA.

[11]  Una-May O'Reilly,et al.  The Troubling Aspects of a Building Block Hypothesis for Genetic Programming , 1994, FOGA.

[12]  David B. Fogel,et al.  Schema processing under proportional selection in the presence of random effects , 1997, IEEE Trans. Evol. Comput..

[13]  Peter A. Whigham,et al.  Grammatical bias for evolutionary learning , 1996 .

[14]  Riccardo Poli,et al.  Schema Theory for Genetic Programming with One-Point Crossover and Point Mutation , 1997, Evolutionary Computation.

[15]  Riccardo Poli,et al.  Schema theorems without expectations , 1999 .

[16]  Riccardo Poli Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms , 2000, FOGA.

[17]  Christopher R. Stephens,et al.  Schemata Evolution and Building Blocks , 1999, Evolutionary Computation.

[18]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

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

[20]  David B. Fogel,et al.  The Schema Theorem and the Misallocation of Trials in the Presence of Stochastic Effects , 1998, Evolutionary Programming.

[21]  Riccardo Poli,et al.  Hyperschema Theory for GP with One-Point Crossover, Building Blocks, and Some New Results in GA Theory , 2000, EuroGP.

[22]  Una-May O'Reilly,et al.  An analysis of genetic programming , 1995 .

[23]  Lee Altenberg,et al.  The Schema Theorem and Price's Theorem , 1994, FOGA.