Genetic algorithm-based optimization for cognitive radio networks
Si Chen, Timothy R. Newman, Joseph Evans, Alexander M. Wyglinski · 2010
Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for a genetic algorithm to reach a decent solution substantially increases with system complexity, and thus techniques are needed that will help facilitate achieving adequate results over a short period of time.