IntroductionGenetic Algorithms (GAs) are adaptive methods that can be used to solve search and optimization problems.GAs are capable of creating solutions to real world problems. The evolution of these solutions to optimal values of the problem depends largely on the proper coding of them.In nature, individuals in a population compete with each other in the quest for resources such as food, water and shelter. Even members of the same species often compete in the search for a partner. Individuals who are more successful in surviving and attracting mates are more likely to generate large numbers of offspring. On the contrary some gifted individuals produce fewer offspring. This means that the genes best adapted individuals in successive generations from spreading to a growing number of individuals.The combination of good features from different ancestors can sometimes produce offspring "knew people", whose adaptation is far greater than any of his ancestors. In this way, species evolve characteristics making increasingly better adapted to the environment in which they live.Why Use Genetic Algorithms?The reason for the growing interest in the AG is that these are global and robust method of finding solutions to problems. The main advantage of these features is the balance struck between efficiency and effectiveness in solving different and very complex problems of large dimensions.What leads to the AG versus other traditional search algorithms is that they differ from those in the following aspects:
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