Genetic algorithms (GA) have become popular tools for search, optimization, machine learning, and solving design problems. These algorithms use simulated evolution to search for solutions to complex problems. A GA is a population-based computational method in which the population, using randomized processes of selection, crossover, and mutation, evolves towards better solutions. In this book, the authors present current research including the application of genetic algorithm optimization techniques in beam steering of circular array antenna; hybrid genetic algorithms; changing range genetic algorithms; study of the influence of forest canopies on the accuracy of GPS measurements using genetic algorithms; roundness evaluation by genetic algorithm; and optimal sizing of analog integrated circuits by applying genetic algorithms.
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