Fitness landscape-based parameter tuning method for evolutionary algorithms for computing unique input output sequences

Jinlong LI, Guanzhou LU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

Abstract

Unique Input Output (UIO) sequences are used in conformance testing of Finite state machines (FSMs). Evolutionary algorithms (EAs) have recently been employed to search UIOs. However, the problem of tuning evolutionary algorithm parameters remains unsolved. In this paper, a number of features of fitness landscapes were computed to characterize the UIO instance, and a set of EA parameter settings were labeled with either 'good' or 'bad' for each UIO instance, and then a predictor mapping features of a UIO instance to 'good' EA parameter settings is trained. For a given UIO instance, we use this predictor to find good EA parameter settings, and the experimental results have shown that the correct rate of predicting 'good' EA parameters was greater than 93%. Although the experimental study in this paper was carried out on the UIO problem, the paper actually addresses a very important issue, i.e., a systematic and principled method of tuning parameters for search algorithms. This is the first time that a systematic and principled framework has been proposed in Search-Based Software Engineering for parameter tuning, by using machine learning techniques to learn good parameter values. © 2011 Springer-Verlag.
Original languageEnglish
Title of host publicationNeural Information Processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II
EditorsBao-Liang LU, Liqing ZHANG, James KWOK
PublisherSpringer Berlin Heidelberg
Pages453-460
Number of pages8
ISBN (Electronic)9783642249587
ISBN (Print)9783642249570
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
Volume7063
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Neural Information Processing, ICONIP 2011
Country/TerritoryChina
CityShanghai
Period13/11/1117/11/11

Keywords

  • Support Vector Machine
  • Evolutionary Algorithm
  • Problem Instance
  • Finite State Machine
  • Input String

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