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Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs

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Parallel Architectures and Bioinspired Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 415))

Abstract

Optimized shape design is used for such applications as wing design in aircraft, hull design in ships, and more generally rotor optimization in turbomachinery such as that of aircraft, ships, and wind turbines.We present work on optimized shape design using a technique from the area of Genetic Programming, self-modifying Cartesian Genetic Programming (SMCGP), to evolve shapes with specific criteria, such as minimized drag or maximized lift. This technique is well suited for a distributed parallel system to increase efficiency. Fitness evaluation of the genetic programming technique is accomplished through a custom implementation of a fluid dynamics solver running on graphics processing units (GPUs). Solving fluid dynamics systems is a computationally expensive task and requires optimization in order for the evolution to complete in a practical period of time. In this chapter, we shall describe both the SMCGP technique and the GPU fluid dynamics solver that together provide a robust and efficient shape design system.

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Authors and Affiliations

  1. IDSIA (Istituto Dalle Molle di Studi sull’Intelligenza Artificiale), Lugano, Switzerland

    Simon Harding

  2. Memorial University of Newfoundland, St. John’s, Canada

    W. Banzhaf

Authors
  1. Simon Harding
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  2. W. Banzhaf
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Correspondence to Simon Harding .

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Editors and Affiliations

  1. Centro Universitario de Mérida, Universidad de Extremadura, C/ Sta Teresa de Jornet 38, Mérida, 06800, Spain

    Francisco Fernández de Vega

  2. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    José Ignacio Hidalgo Pérez

  3. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    Juan Lanchares

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Harding, S., Banzhaf, W. (2012). Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs. In: Fernández de Vega, F., Hidalgo Pérez, J., Lanchares, J. (eds) Parallel Architectures and Bioinspired Algorithms. Studies in Computational Intelligence, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28789-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-28789-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28788-6

  • Online ISBN: 978-3-642-28789-3

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