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Evolutionary Computation

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Evolutionary Decision Trees in Large-Scale Data Mining

Part of the book series: Studies in Big Data ((SBD,volume 59))

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Abstract

A lot of typical problems that have to be commonly solved in engineering or business can be formulated as optimization problems. The performance of an activity or the value of a decision are characterized by a certain cost function, and here, possible alternatives are considered.

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Notes

  1. 1.

    The best-fitted individual is not typically detected in the last generation.

  2. 2.

    A fraction coefficient between zero and one is randomly chosen from the uniform distribution.

  3. 3.

    In genetic programming, bloat can be described as excessive code growth within the individuals of the evolving population without a proportional improvement in fitness, whereas introns are (redundant or unproductive) the parts of the code that do not contribute to the calculation that is being made.

  4. 4.

    A crowding distance can be estimated by the perimeter of a cuboid formed by using the nearest neighbors in the objective space as the vertices.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Marek Kretowski

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  1. Marek Kretowski
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Correspondence to Marek Kretowski .

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Kretowski, M. (2019). Evolutionary Computation. In: Evolutionary Decision Trees in Large-Scale Data Mining. Studies in Big Data, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-030-21851-5_1

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