Title
Exact Schema Theory and Markov Chain Models for Genetic Programming and Variable-length Genetic Algorithms with Homologous Crossover
Abstract
Genetic Programming (GP) homologous crossovers are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. In this paper we present an exact schema theory for GP and variable-length Genetic Algorithms (GAs) which is applicable to this class of operators. The theory is based on the concepts of GP crossover masks and GP recombination distributions that are generalisations of the corresponding notions used in GA theory and in population genetics, as well as the notions of hyperschema and node reference systems, which are specifically required when dealing with variable size representations.In this paper we also present a Markov chain model for GP and variable-length GAs with homologous crossover. We obtain this result by using the core of Vose's model for GAs in conjunction with the GP schema theory just described. The model is then specialised for the case of GP operating on 0/1 trees: a tree-like generalisation of the concept of binary string. For these, symmetries exist that can be exploited to obtain further simplifications.In the absence of mutation, the Markov chain model presented here generalises Vose's GA model to GP and variable-length GAs. Likewise, our schema theory generalises and refines a variety of previous results in GP and GA theory.
Year
DOI
Venue
2004
10.1023/B:GENP.0000017010.41337.a7
Genetic Programming and Evolvable Machines
Keywords
Field
DocType
genetic programming,variable-length genetic algorithms,schema theory,Markov chain,Vose model,mixing matrices.
Crossover,Defining length,Generalization,Computer science,Markov chain,Algorithm,Genetic programming,Artificial intelligence,Operator (computer programming),Machine learning,Genetic algorithm,Homogeneous space
Journal
Volume
Issue
ISSN
5
1
1573-7632
Citations 
PageRank 
References 
18
0.66
16
Authors
3
Name
Order
Citations
PageRank
Riccardo Poli12589308.79
Nicholas Freitag McPhee240432.94
Jonathan E. Rowe345856.35