Predicting CMA-ES operators as inductive biases for shape optimization problems

Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff, Xin Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Domain-dependent expertise knowledge and high-level abstractions to arbitrate between different problem domains can be considered to be essential components of how human problem-solvers build experience and reuse it over the course of their lifetime. However, replicating it from an algorithmic point of view is a less trivial endeavor. Existing knowledge transfer methods in optimization largely fail to provide more specific guidance on specifying the similarity of different optimization problems and the nature of complementary experiences formed on them. A more rigorously grounded approach can be found alternatively in metalearning. This notion neglects any hurdles on characterizing problem similarity in favor of focusing instead on methodology to form domain-dependent inductive biases and mechanisms to arbitrate between them. In principle, we proposed within our previous research methods for constructing inductive biases and predict these from procedural optimization data. However, while we obtained effective methodology, it does not allow the joint construction of predictive components and biases in a cohesive manner. We therefore show in our following study, that improved configurations can be derived for the CMA-ES algorithm which can serve as inductive biases, and that predictors can be trained to recall them. Particularly noteworthy, this scenario allows the construction of predictive component and bias iteratively in a joint manner. We demonstrate the efficacy of this approach in a shape optimization scenario, in which the inductive bias is predicted through an operator configuration in a problem-specific manner during run-time.
Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Electronic)9781728190488
ISBN (Print)9781728190495 (PoD)
DOIs
Publication statusPublished - 24 Jan 2022
Event2021 IEEE Symposium Series on Computational Intelligence (SSCI) - Orlando, FL, USA
Duration: 5 Dec 20217 Dec 2021

Publication series

NameIEEE Symposium Series on Computational Intelligence
ISSN (Electronic)2770-0097

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Period5/12/217/12/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE). It was also supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Shape
  • Training data
  • Focusing
  • Estimation
  • Predictive models
  • Prediction algorithms
  • Search problems
  • Inductive Biases
  • Design Optimization
  • Algorithm Configuration
  • Operator Representation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Predicting CMA-ES operators as inductive biases for shape optimization problems'. Together they form a unique fingerprint.

Cite this