Statistical mechanics theory of genetic algorithms
Theoretical aspects of evolutionary computing
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Evolvable Machines
A Fixed Point Analysis Of A Gene Pool GA With Mutation
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Schemata evolution and building blocks
Evolutionary Computation
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Although much progress has been made in recent years in the theory of GAs and GP, there is still a conspicuous lack of tools with which to derive systematic, approximate solutions to their dynamics. In this article we propose and study perturbation theory as a potential tool to fill this gap. We concentrate mainly on selection-mutation systems, showing different implementations of the perturbative framework, developing, for example, perturbative expansions for the eigenvalues and eigenvectors of the transition matrix. The main focus however, is on diagrammatic methods, taken from physics, where we show how approximations can be built up using a pictorial representation generated by a simple set of rules, and how the renormalization group can be used to systematically improve the perturbation theory.