Abstract
Today, in 2013, when this small book is being written, evolutionary algorithms are established as a well-known and widely used class of heuristics, inspired by the model of organic evolution, for solving optimization problems. And this really means that these algorithms are regularly used in real-world applications, and some algorithmic variants have been incorporated into standardized off-the-shelf software toolboxes. Between 1990—when the first author of this book entered into the field which was known under the term “genetic algorithms” only—and today, the field has seen tremendous development and has earned enormous scientific recognition.
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Notes
- 1.
As the reader will realize, the numbers used here are still much larger than what would often be available in real-world applications (up to a few hundreds), but they are much smaller than what is typically used for comparing algorithms.
- 2.
For Black-Box-Optimization Benchmarking (BBOB) [34], the recommended number of function evaluations is 106 n, for n-dimensional test problems.
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Bäck, T., Foussette, C., Krause, P. (2013). Introduction. In: Contemporary Evolution Strategies. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40137-4_1
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