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Advanced Evolutionary Algorithms in Data Mining

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Abbreviations

Control parameter:

Control parameter determines behavior of evolutionary program (e.g., crossover rate, population size, etc.).

Differential evolution:

An evolutionary algorithm for global optimization, which realized the evolution of a population of individuals using mutation, crossover, and selection operation.

Evolutionary computation:

Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies an algorithm to find the fittest models from the set to serve as inputs to the next generation, ultimately leading to a model that best represents the data.

Individual:

An individual represents a candidate solution. During the optimization process an evolutionary algorithm usually uses a population of individuals to solve a particular problem.

Search space:

Set of all possible situations of the optimization problem that we want to solve.

Self-adaptation:

The ability that allows an evolutionary algorithm to adapt itself to any general class of problems, by reconfiguring itself accordingly, and do this without user’s interaction. Self-adaptation is applied on control parameter(s).

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Institute of Computer Science, University of Maribor, Smetanova ul. 17, 2000, Maribor, Slovenia

    Janez Brest

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  1. Janez Brest
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Correspondence to Janez Brest .

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Editors and Affiliations

  1. Ramtech Ltd., Larkspur, California, USA

    Robert A. Meyers

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Brest, J. (2015). Advanced Evolutionary Algorithms in Data Mining. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_650-1

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  • DOI: https://doi.org/10.1007/978-3-642-27737-5_650-1

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  • Online ISBN: 978-3-642-27737-5

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