Abstract
Evolutionary algorithms are search methods that can be used for solving optimization problems. They mimic working principles from natural evolution by employing a population—based approach, labeling each individual of the population with a fitness and including elements of random, albeit the random is directed through a selection process. In this chapter, we review the basic principles of evolutionary algorithms and discuss their purpose, structure and behavior. In doing so, it is particularly shown how the fundamental understanding of natural evolution processes has cleared the ground for the origin of evolutionary algorithms. Major implementation variants and their structural as well as functional elements are discussed. We also give a brief overview on usability areas of the algorithm and end with some general remarks of the limits of computing.
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Zelinka, I., Richter, H. (2010). Evolutionary Algorithms for Chaos Researchers. In: Zelinka, I., Celikovsky, S., Richter, H., Chen, G. (eds) Evolutionary Algorithms and Chaotic Systems. Studies in Computational Intelligence, vol 267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10707-8_2
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