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Efficient Computing Budget Allocation for Finding Simplest Good Designs.

Qing-Shan Jia1, Enlu Zhou, Chun-Hung Chen

  • 1Center for Intelligent and Networked Systems (CFINS), Department of Automation, TNLIST, Tsinghua University, Beijing 100084, China (Tel.: +86-10-62773006, fax: +86-10-62796115.

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Summary
This summary is machine-generated.

This study introduces methods for finding the simplest good designs in simulation-based optimization (SBO). It provides theoretical bounds and efficient algorithms to identify the simplest designs with top performance.

Keywords:
Simulation-based optimizationcomplexity preferenceoptimal computing budget allocationwireless sensor network

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Area of Science:

  • Optimization and Simulation
  • Computer Science
  • Engineering

Background:

  • Simple designs are preferred in simulation-based optimization (SBO) for efficiency and resource conservation.
  • Existing research primarily focuses on finding good designs, with limited attention to identifying the simplest ones.

Purpose of the Study:

  • To address the problem of finding the simplest good designs in SBO.
  • To develop and evaluate methods for selecting the m simplest designs with top performance.
  • To provide theoretical insights into the selection probabilities of simple designs.

Main Methods:

  • Derivation of lower bounds for probabilities of selecting the m simplest good designs.
  • Development of two efficient computing budget allocation methods for identifying simplest good designs.
  • Asymptotic optimality analysis of the proposed methods.

Main Results:

  • Established theoretical lower bounds for selecting simplest good designs.
  • Demonstrated the asymptotic optimality of the developed budget allocation methods.
  • Empirically compared method performance on academic examples and a real-world wireless sensor network problem.

Conclusions:

  • The study offers valuable contributions to the field of finding simplest good designs in SBO.
  • The proposed methods and theoretical bounds can guide practitioners in selecting efficient and effective designs.
  • This work provides a foundation for future research in the area of design simplicity in optimization.