Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithm <s> Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception. > <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Genetic algorithm <s> Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics. The literature survey is accompanied by the presentation of references for further details, including applications. Recent trends are also briefly discussed. <s> BIB002
The Genetic Algorithm (GA)  BIB001 is one of the well-known and mostly used population-based technique. In GA, a solution of an optimization problem is represented by a chromosome. A set of chromosomes forms the population. Two basic yet very important operations in GA are: crossover and mutation. The crossover operation combines previously selected individuals together by exchanging some of their parts. Mutation, on the other hand, brings some randomness into the search to avoid the problem of local optima. The important factors for implementing any GA are: the selection strategy and the type of crossover and mutation operators BIB002 .