The evolution of evolvability in genetic programming
Advances in genetic programming
An introduction to genetic algorithms
An introduction to genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A derived Markov process for modeling reaction networks
Evolutionary Computation
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Coarse-graining in genetic algorithms: some issues and examples
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Sufficient conditions for coarse-graining evolutionary dynamics
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
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The use of genotypic populations is necessary for adaptation in Evolutionary Algorithms. We use a technique called form-invariant commutation to study the immediate effect of evolutionary operations on populations of genotypes. This technique allows us to understand compositional changes induced by evolutionary operations in terms of constraints between populations. Deep insight into the population-level effect of some evolutionary operation is possible when multiple constraints can be derived for all pairs of pre and post operative populations; for each such pair of populations the constraints between them are then said to hold simultaneously. When selection is fitness proportional we show that any coarse-graining of the genotype set can be used to systematically derive single constraints between between all pairs of pre and post selection populations. Matters are not so simple in the case of variation. We develop an abstract condition called ambivalence and show that when a coarse-graining and a variation operation satisfy this condition then a systematic derivation of single constraints between all pairs of pre and post variation populations is possible. Finally we show that it is possible to use schema partitions to systematically derive simultaneous constraints for any combination of variation operations that are commonly used in GAs.