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Evolutionary-Based Methods

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Book cover Computational Methods for Application in Industry 4.0

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSMANUFACT))

Abstract

In the current section, several metaheuristics involving the evolutionary of a population in order to create new generations of genetically superior individuals are presented. These algorithms are usually significantly influenced by the most prominent (and earliest) among them, the Genetic Algorithm (GA). Details about their basic characteristics and function, as well as some important variants, are described and applications in the field of industrial engineering are highlighted. A detailed description of the basic features of the genetic algorithm is presented at the beginning of this chapter and afterwards, other Evolutionary Algorithms (EA) are summarized. In specific, both relatively older and well established, as well as newer but promising methods are included, namely Differential Evolutionary, Memetic Algorithm, Imperialist Competitive Algorithm, Biogeography-Based Optimization algorithm, Teaching-Learning-Based optimization, Sheep Flock Heredity algorithm, Shuffled Frog-Leaping algorithm, and Bacteria Foraging Optimization algorithm.

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Authors and Affiliations

  1. Laboratory of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece

    Nikolaos E. Karkalos & Angelos P. Markopoulos

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

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  1. Nikolaos E. Karkalos
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  2. Angelos P. Markopoulos
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Correspondence to Nikolaos E. Karkalos .

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Karkalos, N.E., Markopoulos, A.P., Davim, J.P. (2019). Evolutionary-Based Methods. In: Computational Methods for Application in Industry 4.0. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-92393-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-92393-2_2

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