Abstract
The preceding chapter presented all relevant elements of evolutionary algorithms, namely guidelines of how to choose an encoding for the solution candidates, procedures how to select individuals based on their fitness, and genetic operators with which modified solution candidates can be obtained. Equipped with these ingredients we proceed in this chapter to introducing basic forms of evolutionary algorithms, including classical genetic algorithms (in which solution candidates are encoded as bit strings), evolution strategies (which focus on numerical optimization) and genetic programming (which tries to derive function expressions or even (simple) program structures with evolutionary principles). Finally, we take a look at related population-based approaches (like ant colony and particle swarm optimization).
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Notes
- 1.
Regression finds a function from a given class to given data by minimizing the sum of squared deviations and is also called the method of least squares, see Sect. 10.2.
- 2.
Note that this is exactly opposite to evolution strategies (see Sect. 13.2), in which crossover is often abandoned and mutation is the only genetic operator.
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Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P. (2013). Fundamental Evolutionary Algorithms. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5013-8_13
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