Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to genetic algorithms
An introduction to genetic algorithms
Foundations of genetic programming
Foundations of genetic programming
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Group properties of crossover and mutation
Evolutionary Computation
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
Backward-chaining evolutionary algorithms
Artificial Intelligence
Understanding the biases of generalised recombination: part I
Evolutionary Computation
On Crossover Success Rate in Genetic Programming with Offspring Selection
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Backward-chaining evolutionary algorithms
Artificial Intelligence
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
A new method for modeling the behavior of finite population evolutionary algorithms
Evolutionary Computation
Predicting problem difficulty for genetic programming applied to data classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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.