Abstract
One of the most exciting aspects of life is its evolutionary nature where the individuals keep improving along with generations. Genetic Algorithms are an inspiration from this natural evolution and find themselves as powerful optimizing agents for solving numerous real life applications. These algorithms can model complex problems and return the optimal solution in an iterative manner. This chapter presents the manner in which we model and solve the problem using this evolutionary technique. The role of the various parameters and optimal parameter setting as per the problem requirements would be discussed. The chapter would present mutation, selection, crossover and other genetic operators. Evolution forms the base for most of the complex systems that are designed to evolve with time. In this chapter we hence first study the basic concepts and then take an inspiration towards evolving systems. At the same time we present the limitations of evolution that marks a threshold to massive potential of problem solving that these algorithms have.
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Shukla, A., Tiwari, R., Kala, R. (2010). Genetic Algorithm. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_3
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DOI: https://doi.org/10.1007/978-3-642-14344-1_3
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