A Bayesian Restarting Approach to Algorithm Selection

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationSimulated Evolution and Learning
EditorsYuhui Shi, Kay Chen Tan, Mengjie Zhang, Ke Tang, Xiaodong Li, Qingfu Zhang, Ying Tan, Martin Middendorf, Yaochu Jin
PublisherSpringer, Cham
Pages397-408
ISBN (Electronic)978-3-319-68759-9
ISBN (Print)978-3-319-68758-2
Publication statusPublished - Nov 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10593
ISSN (Print)0302-9743

Conference

Title11th International Conference on Simulated Evolution and Learning ( SEAL 2017)
LocationSouthern University of Science and Technology
PlaceChina
CityShenzhen
Period10 - 13 November 2017

Abstract

A Bayesian algorithm selection framework for black box optimization problems is proposed. A set of benchmark problems is used for training. The performance of a set of algorithms on the problems is recorded. In the beginning, an algorithm is randomly selected to run on the given unknown problem. A Bayesian approach is used to measure the similarity between problems. The most similar problem to the given problem is selected. Then the best algorithm for solving it is suggested for the second run. The process repeats until n algorithms have been run. The best solution out of n runs is recorded. We have experimentally evaluated the property and performance of the framework. Conclusions are (1) it can identify the most similar problem efficiently; (2) it benefits from a restart mechanism. It performs better when more knowledge is learned. Thus it is a good algorithm selection framework.

Research Area(s)

  • Algorithm selection, Bayesian approach, Evolutionary algorithm, Monte Carlo method, Optimization problems

Citation Format(s)

A Bayesian Restarting Approach to Algorithm Selection. / He, Yaodong; Yuen, Shiu Yin; Lou, Yang.

Simulated Evolution and Learning. ed. / Yuhui Shi; Kay Chen Tan; Mengjie Zhang; Ke Tang; Xiaodong Li; Qingfu Zhang; Ying Tan; Martin Middendorf; Yaochu Jin. Springer, Cham, 2017. p. 397-408 (Lecture Notes in Computer Science; Vol. 10593).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review