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Review of Industrial Design Optimization by Genetic Algorithms

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Advances on Mechanics, Design Engineering and Manufacturing II

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

In engineering, genetic algorithms (GA) have been successfully applied to some cases. The current state of this technique has evolved to allow computer designs from a sketch. Thus, GA generate a solution by optimization. Here the final solution is restricted by the final specifications. While CAD systems employ basic useful parameters to allow users to build the final design, GA utilizes preliminary designs from the beginning. CAD systems use primitives (points, lines and splines), which are controlled by users to build the design. In an evolutionary design system, it is GA that must modify designs to reach the final solution. When GA reach the solution, the design meets the final specifications. For this reason, the representation of an evolutionary design system based on GA must have a good parameter definition. Compared to the configuration design, a preliminary design is more difficult to computerize given its more marked emphasis on creativity. Therefore, the first step is to identify the ways to computerize the process involved in design. A bibliographic review sets the basis of using GA in the industrial design process.

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Author information

Authors and Affiliations

  1. Graphical Engeniering Department, Technical University of Cartagena, Murcia, Spain

    F. L. Sáez-Gutiérrez & F. J. F. Cañavate

  2. Automation and Systems Engeniering Department, Technical University of Cartagena, Murcia, Spain

    A. Guerrero-González

Authors
  1. F. L. Sáez-Gutiérrez
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  2. F. J. F. Cañavate
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  3. A. Guerrero-González
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Corresponding author

Correspondence to F. L. Sáez-Gutiérrez .

Editor information

Editors and Affiliations

  1. Departamento de Expresión Gráfica, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain

    Francisco Cavas-Martínez

  2. Département Génie des Systèmes Mécaniques, Université de Technologie de Compiègne, Compiègne, Oise, France

    Benoit Eynard

  3. Departamento de Expresión Gráfica, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain

    Francisco J. Fernández Cañavate

  4. Departamento de Expresión Gráfica, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain

    Daniel G. Fernández-Pacheco

  5. Departamento de Mecánica, Tecnun—Universidad de Navarra, San Sebastián—Donostia, Guipúzcoa, Spain

    Paz Morer

  6. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Università degli Studi di Palermo, Palermo, Italy

    Vincenzo Nigrelli

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Sáez-Gutiérrez, F.L., Cañavate, F.J.F., Guerrero-González, A. (2019). Review of Industrial Design Optimization by Genetic Algorithms. In: Cavas-Martínez, F., Eynard, B., Fernández Cañavate, F., Fernández-Pacheco, D., Morer , P., Nigrelli, V. (eds) Advances on Mechanics, Design Engineering and Manufacturing II. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-12346-8_33

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  • DOI: https://doi.org/10.1007/978-3-030-12346-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12345-1

  • Online ISBN: 978-3-030-12346-8

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