  In recent times, wireless access technology is becoming increasingly
commonplace due to the ease of operation and installation of untethered
wireless media. The design of wireless networking is challenging due to the
highly dynamic environmental condition that makes parameter optimization a
complex task. Due to the dynamic, and often unknown, operating conditions,
modern wireless networking standards increasingly rely on machine learning and
artificial intelligence algorithms. Genetic algorithms (GAs) provide a
well-established framework for implementing artificial intelligence tasks such
as classification, learning, and optimization. GAs are well-known for their
remarkable generality and versatility, and have been applied in a wide variety
of settings in wireless networks. In this paper, we provide a comprehensive
survey of the applications of GAs in wireless networks. We provide both an
exposition of common GA models and configuration and provide a broad ranging
survey of GA techniques in wireless networks. We also point out open research
issues and define potential future work. While various surveys on GAs exist in
literature, our paper is the first paper, to the best of our knowledge, which
focuses on their application in wireless networks.
