Research Article
Population Adaptation for Genetic Algorithm-based Cognitive Radios
@INPROCEEDINGS{10.1109/CROWNCOM.2007.4549811, author={Timothy R. Newman and Rakesh Rajbanshi and Alexander M. Wyglinski and Joseph B. Evans and Gary J. Minden}, title={Population Adaptation for Genetic Algorithm-based Cognitive Radios}, proceedings={2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2008}, month={6}, keywords={Algorithm design and analysis Analytical models Cognition Cognitive radio Decision making Engines Genetic algorithms Information technology Space technology Wireless sensor networks}, doi={10.1109/CROWNCOM.2007.4549811} }- Timothy R. Newman
Rakesh Rajbanshi
Alexander M. Wyglinski
Joseph B. Evans
Gary J. Minden
Year: 2008
Population Adaptation for Genetic Algorithm-based Cognitive Radios
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2007.4549811
Abstract
Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.