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Genetic algorithm-based optimization for cognitive radio networks

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.