Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Evolutionary algorithms in SDN <s> Energy consumption is a key concern in the deployment and operation of current data networks, for which software-defined networks (SDNs) have become a promising alternative. Although several works have been proposed to improve the energy efficiency, these techniques may lead to performance degradations when QoS requirements are neglected. Inspired by this problem, this letter introduces a new routing strategy, jointly considering QoS requirements and energy awareness in SDN with in-band control traffic. To that end, we present a complete formulation of the optimization problem and implement a multi-objective evolutionary algorithm. Simulation results validate the performance improvement on critical network parameters. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Evolutionary algorithms in SDN <s> Moving Target Defense (MTD) is a paradigm in cyber-security that proposes cyber-systems that continually change their configurations to avoid easy surveillance from adversaries. Despite its promise, MTDs have yet to be fully adopted in real world systems. Using the concepts from the general theory of MTD, this paper proposes that the goal of MTDs i.e continually adapting to their environment to evade attack is similar to the concept of an organism evolving to survive in its habitat. This implies that Evolutionary Computation (EC) techniques could be used to fulfill the goal of MTD, specifically in relation to intrusion detection in the emerging field of Software Defined Networks (SDNs). The programmable nature of SDNs provide a degree of flexibility not possible in conventional networks. Thus, making it possible to completely automate the configuration of such networks. <s> BIB002
The evolutionary algorithms were employed for solving controller placement problem in distributed SDNs  , routing BIB001 and moving target defence BIB002 . Zhang et al.  investigated the problem of controller placement by taking into consideration controller-to-switch and controller-to-controller delays for wide area networks, where they proposed two evolutionary algorithms. The first one, denoted as Evo-Place, finds a set of Pareto controller placements. The second one, denoted as Best-Reactivity, finds the final placement that minimizes the average reaction time perceived at the switches. The experimental study showed that a better Pareto frontier obtained by Evo-Place compared to Rnd-Place (a basic randomized algorithm), given the same number of considered placements and the same number of iterations. Fernandez-Fernandez et al. BIB001 presented an SDN-based routing strategy that considers both QoS requirements and energy awareness. They employed a Multi-Objective Evolutionary Algorithms (MOEA) based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2). They employed two objective functions. The first one is based on the performance requirements for the communications between the control and data planes. The second objective, related with energy awareness, aims at minimizing the number of links that need to be activated when a connection request arrives. The proposed routing algorithm significantly outperformed a modified shortest path routing in terms of accepted demands. Makanju et al. BIB002 suggested using evolutionary computation (EC) techniques for moving target defence in SDN. Moving target defence (MTD) systems have three main challenges: 1) how to choose another configuration, 2) adapting to next configuration, and 3) when to start the adoption. They mentioned that a multi-objective genetic algorithms (MOGA) can be suitable for MTDs requirements.