The evolution of evolvability in genetic programming
Advances in genetic programming
An introduction to genetic algorithms
An introduction to genetic algorithms
Statistical mechanics theory of genetic algorithms
Theoretical aspects of evolutionary computing
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
Theme preservation and the evolution of representation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Differentiable coarse graining
Theoretical Computer Science - Foundations of genetic algorithms
EC theory: a unified viewpoint
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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
Explaining optimization in genetic algorithms with uniform crossover
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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It is commonly assumed that the ability to track the frequencies of a set of schemata in the evolving population of an infinite population genetic algorithm (IPGA) under different fitness functions will advance efforts to obtain a theory of adaptation for the simple GA. Unfortunately, for IPGAs with long genomes and non-trivial fitness functions there do not currently exist theoretical results that allow such a study. We develop a simple framework for analyzing the dynamics of an infinite population evolutionary algorithm (IPEA). This framework derives its simplicity from its abstract nature. In particular we make no commitment to the data-structure of the genomes, the kind of variation performed, or the number of parents involved in a variation operation. We use this framework to derive abstract conditions under which the dynamics of an IPEA can be coarse-grained. We then use this result to derive concrete conditions under which it becomes computationally feasible to closely approximate the frequencies of a family of schemata of relatively low order over multiple generations, even when the bitstsrings in the evolving population of the IPGA are long.