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A learning classifier system for three-dimensional shape optimization

  • Applications of Evolutionary Computation Evolutionary Computation in Mechanical, Chemical, Biological, and Optical Engineering
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Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

A learning classifier system complex is developed in order to accomplish the broader goal of developing a methodology to perform generalized zeroth-order two- and three-dimensional shape optimization. Specifically, the methodology has the objective of determining the optimal boundary to minimize mass while satisfying constraints on stress and geometry. Even with the enormous advances in shape optimization no method has proven to be satisfactory across the broad spectrum of optimization problems facing the modern engineer. Similarly the available software in the field of learning classifier systems is so embryonic that a new software package had to be developed for this application. The shape optimization via hypothesizing inductive classifier system complex (SPHINcsX) instantiates the methodology in a software package overcoming many of the limitations of today's conventional shape optimization techniques, while advancing the state-of-the-art in classifier system software tools.

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Authors and Affiliations

  1. Stanford University, PO Box 9723, 94309-9723, Stanford, CA, USA

    Robert A. Richards & Sheri D. Sheppard

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  1. Robert A. Richards
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  2. Sheri D. Sheppard
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Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Richards, R.A., Sheppard, S.D. (1996). A learning classifier system for three-dimensional shape optimization. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1066

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  • DOI: https://doi.org/10.1007/3-540-61723-X_1066

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

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