Computer Science – Neural and Evolutionary Computing
Scientific paper
2006-12-21
Computer Science
Neural and Evolutionary Computing
19 pages, 1 figure. Accepted to the Foundations of Genetic Algorithms Conference 2007 (FOGA IX)
Scientific paper
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.
No associations
LandOfFree
Sufficient Conditions for Coarse-Graining Evolutionary Dynamics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Sufficient Conditions for Coarse-Graining Evolutionary Dynamics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sufficient Conditions for Coarse-Graining Evolutionary Dynamics will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-21822