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Three-dimensional shape optimization utilizing a learning classifier system

Authors:

Robert A. Richards,

Sheri D. SheppardAuthors Info & Claims

Proceedings of the 1st annual conference on genetic programming

Pages 539 - 546

Published: 28 July 1996 Publication History

Abstract

A methodology to perform generalized zeroth-order two- and three-dimensional shape optimization utilizing a learning classifier system is presented and applied. 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. 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. From environmental input, and a population of initially randomly generated rules, SPHINcsX is expected to learn to make the appropriate component shape modifications to reach a minimum mass design while satisfying all stress constraints. SPHINcsX not only learns from a clean slate, but confronts the additional challenge of learning without sensitivity information that most other shape optimization algorithms deem essential. SPHINcsX has proven adept at solving both two- and three-dimensional shape optimization problems.

References

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Digital Library

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Richards, Robert A., Zeroth-Order Shape Optimization Utilizing a Learning Classifier System, Ph.D. Dissertation, Stanford University, Stanford, CA, 1995.

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Cited By

Lanzi PRiolo R(2000)A Roadmap to the Last Decade of Learning Classifier System ResearchLearning Classifier Systems, From Foundations to Applications10.5555/646371.689039(33-62)Online publication date: 1-Jan-2000
https://dl.acm.org/doi/10.5555/646371.689039
Kovacs TLanzi P(2000)A Learning Classifier Systems BibliographyLearning Classifier Systems, From Foundations to Applications10.5555/646371.689037(321-348)Online publication date: 1-Jan-2000
https://dl.acm.org/doi/10.5555/646371.689037

Three-dimensional shape optimization utilizing a learning classifier system
1. Computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image Guide Proceedings

Proceedings of the 1st annual conference on genetic programming

July 1996

571 pages

ISBN:0262611279

General Chair:
John R. Koza
Stanford University

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 28 July 1996

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Lanzi PRiolo R(2000)A Roadmap to the Last Decade of Learning Classifier System ResearchLearning Classifier Systems, From Foundations to Applications10.5555/646371.689039(33-62)Online publication date: 1-Jan-2000
https://dl.acm.org/doi/10.5555/646371.689039
Kovacs TLanzi P(2000)A Learning Classifier Systems BibliographyLearning Classifier Systems, From Foundations to Applications10.5555/646371.689037(321-348)Online publication date: 1-Jan-2000
https://dl.acm.org/doi/10.5555/646371.689037

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