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Integrated pre-design step methodology based onmulti-objective evolutionary optimization

Authors:

David Mosnier,

Frédéric Gillot,

Antoine Ducloux,

Mohamed IchchouAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 1317 - 1318

https://doi.org/10.1145/1830483.1830721

Published: 07 July 2010 Publication History

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Abstract

In this paper we propose an integrated pre-design step framework using multiobjective evolutionary optimization and a decision support tool. The tailored genetic algorithm relies on specific fitness function which enables to deal with a high number of objectives. Moreover, surrogate models have been integrated so as to speed up objective functions evaluations which are usually expensive in case of mechanical product pre-design step. An automatic post-treatment of Pareto optimal solutions is proposed in order to synthesize a large multidimensional database into a restricted number of typings. This latter step is of particular importance since it affords designer a powerful decision support tool.

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Index Terms

Integrated pre-design step methodology based onmulti-objective evolutionary optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
  2. Machine learning
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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

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Hypervolume-based local search in multi-objective evolutionary optimization

Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems

A reference points-based evolutionary algorithm for many-objective optimization

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