General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part II

This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.

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

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

[3]  William B. Langdon,et al.  Quadratic Bloat in Genetic Programming , 2000, GECCO.

[4]  Byoung-Tak Zhang,et al.  Balancing Accuracy and Parsimony in Genetic Programming , 1995, Evolutionary Computation.

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

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

[7]  Riccardo Poli,et al.  Exact Schema Theorem and Effective Fitness for GP with One-Point Crossover , 2000, GECCO.

[8]  Riccardo Poli,et al.  A schema theory analysis of mutation size biases in genetic programming with linear representations , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  Peter Nordin,et al.  A compiling genetic programming system that directly manipulates the machine-code , 1994 .

[10]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[11]  R. Poli,et al.  Markov chain models for GP and variable-length GAs with homologous crossover , 2001 .

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

[13]  José Carlos Príncipe,et al.  A Markov Chain Framework for the Simple Genetic Algorithm , 1993, Evolutionary Computation.

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

[15]  Riccardo Poli,et al.  General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part I , 2003, Evolutionary Computation.

[16]  Terence Soule,et al.  Code growth in genetic programming , 1996 .

[17]  Nicholas Freitag McPhee,et al.  Accurate Replication in Genetic Programming , 1995, ICGA.

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

[19]  Lothar Thiele,et al.  Genetic Programming and Redundancy , 1994 .

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

[21]  R. Poli Why the schema theorem is correct also in the presence of stochastic effects , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[22]  M. O'Neill,et al.  Grammatical evolution , 2001, GECCO '09.

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

[24]  Michael D. Vose,et al.  Modeling genetic algorithms with Markov chains , 1992, Annals of Mathematics and Artificial Intelligence.

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

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

[27]  William B. Langdon,et al.  Genetic Programming Bloat without Semantics , 2000, PPSN.

[28]  Rafael A. Perez,et al.  The schema theorem considered insufficient , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[29]  Riccardo Poli,et al.  Exact Schema Theorems for GP with One-Point and Standard Crossover Operating on Linear Structures and Their Application to the Study of the Evolution of Size , 2001, EuroGP.

[30]  Riccardo Poli,et al.  The evolution of size and shape , 1999 .

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

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

[33]  Peter A. Whigham,et al.  Search bias, language bias and genetic programming , 1996 .

[34]  Riccardo Poli,et al.  A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming , 2003, EuroGP.

[35]  Riccardo Poli,et al.  Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover , 2001, Genetic Programming and Evolvable Machines.

[36]  R. Poli,et al.  Exact GP schema theory for headless chicken crossover and subtree mutation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[37]  W. Langdon,et al.  Genetic Programming with One-Point Crossover , 1998 .

[38]  Riccardo Poli,et al.  Using Schema Theory To Explore Interactions Of Multiple Operators , 2002, GECCO.

[39]  Riccardo Poli,et al.  A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations , 2001, EuroGP.

[40]  R. Poli,et al.  Exact schema theory for GP and variable-length GAs with homologous crossover , 2001 .

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

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

[43]  Alden H. Wright,et al.  Allele Diffusion in Linear Genetic Programming and Variable-Length Genetic Algorithms with Subtree Crossover , 2002, EuroGP.

[44]  H. Geiringer On the Probability Theory of Linkage in Mendelian Heredity , 1944 .

[45]  Prügel-Bennett,et al.  Analysis of genetic algorithms using statistical mechanics. , 1994, Physical review letters.

[46]  L. Altenberg EMERGENT PHENOMENA IN GENETIC PROGRAMMING , 1994 .

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

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

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

[50]  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.

[51]  Hartmut Pohlheim,et al.  Visualization of evolutionary algorithms - set of standard techniques and multidimensional visualization , 1999 .

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

[53]  Riccardo Poli,et al.  General Schema Theory for Genetic Programming with Subtree-Swapping Crossover , 2001, EuroGP.

[54]  B. W.,et al.  Size Fair and Homologous Tree Genetic Programming Crossovers , 1999 .

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

[56]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.