Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Cognitive engine implementation for wireless multicarrier transceivers
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Genetic algorithm-based optimization for cognitive radio networks
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Cognitive Radio Engine Design Based on Ant Colony Optimization
Wireless Personal Communications: An International Journal
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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 substantially 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, which represents the amount of change in the environment parameters since the previous cognition cycle.